{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "a = np.array([3,4,5])\n",
    "plt.figure()\n",
    "plt.plot(a,a)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "data = {}\n",
    "data['dbpedia'] = {}\n",
    "data['dbpedia']['mean'] = [float(x) for x in \"0.805\t0.809\t0.810\t0.805\t0.798\t0.790\t0.781\t0.773\t0.762\t0.751\t0.740\t0.728\t0.715\t0.699\t0.679\t0.653\t0.621\t0.575\".split(\"\\t\")]\n",
    "data['dbpedia']['std'] =  [float(x) for x in \"0.038\t0.040\t0.039\t0.038\t0.035\t0.032\t0.028\t0.024\t0.022\t0.021\t0.023\t0.025\t0.028\t0.034\t0.042\t0.050\t0.062\t0.069\".split('\\t')]\n",
    "data['dbpedia']['baseline'] = 0.756\n",
    "data['dbpedia']['style'] = ['^', 'blue']\n",
    "data['dbpedia']['legend'] = \"DBPedia\"\n",
    "\n",
    "\n",
    "data['agnews'] = {}\n",
    "data['agnews']['mean'] = [float(x) for x in \"0.822\t0.830\t0.831\t0.831\t0.830\t0.827\t0.825\t0.822\t0.819\t0.816\t0.811\t0.806\t0.802\t0.796\t0.788\t0.782\t0.771\t0.751\".split('\\t')]\n",
    "data['agnews']['std'] = [float(x) for x in \"0.015\t0.015\t0.016\t0.016\t0.019\t0.021\t0.025\t0.024\t0.026\t0.027\t0.027\t0.026\t0.026\t0.026\t0.027\t0.024\t0.023\t0.023\".split('\\t')]\n",
    "data['agnews']['baseline'] = 0.799\n",
    "data['agnews']['style'] = ['o', 'orange']\n",
    "data['agnews']['legend'] = \"AG's News\"\n",
    "\n",
    "data['amazon'] = {}\n",
    "data['amazon']['mean'] = [float(x) for x in \"0.918\t0.921\t0.923\t0.924\t0.925\t0.925\t0.925\t0.926\t0.926\t0.926\t0.926\t0.926\t0.926\t0.925\t0.925\t0.925\t0.924\t0.923\".split('\\t')]\n",
    "data['amazon']['std'] = [float(x) for x in \"0.021\t0.021\t0.020\t0.020\t0.020\t0.020\t0.020\t0.019\t0.019\t0.019\t0.019\t0.019\t0.019\t0.019\t0.019\t0.018\t0.019\t0.020\".split('\\t')]\n",
    "data['amazon']['baseline'] = 0.913\n",
    "data['amazon']['style'] = ['+','green']\n",
    "data['amazon']['legend'] = \"Amazon\"\n",
    "\n",
    "data['imdb'] = {}\n",
    "data['imdb']['mean'] = [float(x) for x in \"0.902\t0.907\t0.909\t0.910\t0.911\t0.913\t0.913\t0.914\t0.914\t0.915\t0.916\t0.916\t0.916\t0.916\t0.917\t0.917\t0.918\t0.917\".split('\\t')]\n",
    "data['imdb']['std'] =  [float(x) for x in \"0.042\t0.039\t0.037\t0.036\t0.035\t0.034\t0.033\t0.033\t0.033\t0.033\t0.033\t0.033\t0.033\t0.033\t0.033\t0.034\t0.033\t0.035\".split(\"\\t\")]\n",
    "data['imdb']['baseline'] = 0.908\n",
    "data['imdb']['style'] = ['x','purple']\n",
    "data['imdb']['legend'] = 'IMDB'\n",
    "\n",
    "data['xaxis'] = np.array([i/20 for i in range(1,19)])\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "plt.figure()\n",
    "for dataset in ['agnews', 'dbpedia', 'imdb', 'amazon']:\n",
    "    mean = np.array(data[dataset]['mean'])\n",
    "    plt.plot(data['xaxis'], mean, data[dataset]['style'][0]+'-',color=data[dataset]['style'][1],label=data[dataset]['legend'],alpha=0.8)\n",
    "    baseline = np.zeros_like(data['xaxis'])+data[dataset]['baseline']\n",
    "    plt.plot(data['xaxis'], baseline, \"--\",color=data[dataset]['style'][1], alpha=0.5)\n",
    "\n",
    "\n",
    "\n",
    "plt.xlim(0, 0.95)\n",
    "plt.ylim(0.7,0.95)\n",
    "plt.legend()\n",
    "plt.ylabel(\"Micro-F1\")\n",
    "plt.xlabel(r\"Top-$k$/$|\\mathcal{V}|$\")\n",
    "# plt.show()\n",
    "plt.savefig(\"../plots/hyperpara-k-2.pdf\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# below for codes for top word vis_new"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pickle as pkl\n",
    "file = \"../outputlogs/zs_KPT_mean_toppredwords_agnews/00006/visualize_top_choice/dataset_agnews_temp_0_seed_144.pkl\"\n",
    "with open(file, 'rb')  as fin:\n",
    "    zip_data = pkl.load(fin)\n",
    "\n",
    "mykeys = list(zip_data.keys())\n",
    "\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "features = []\n",
    "import csv\n",
    "with open(\"../datasets/agnews/test.csv\", encoding='utf8') as f:\n",
    "    reader = csv.reader(f, delimiter=',')\n",
    "    for idx, row in enumerate(reader):\n",
    "        label, headline, body = row\n",
    "        text_a = headline.replace('\\\\', ' ')\n",
    "        text_b = body.replace('\\\\', ' ')\n",
    "        features.append({\"text_a\": text_a, \"text_b\":text_b, \"label\":int(label)-1})\n",
    "\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "words_ranks = {}\n",
    "words_ranks_correct = {}\n",
    "\n",
    "sorted_wordss = []\n",
    "\n",
    "for gtclass in range(4):\n",
    "    words_ranks_les_10 = {}\n",
    "    for word in zip_data:\n",
    "        words_ranks_les_10[word]= 0 \n",
    "        for rank, pred, truth, idx in zip_data[word]:\n",
    "            if truth==gtclass and pred==gtclass and rank<5:\n",
    "                words_ranks_les_10[word] += 1\n",
    "    sorted_words = sorted(words_ranks_les_10.items(), key=lambda x:-x[1])\n",
    "    wordname, wordfreq = list(zip(*sorted_words))\n",
    "    sorted_wordss.append([[w[1:] for w in wordname], [x for x in wordfreq]])\n",
    "\n",
    "# sorted_wordss|\n",
    "\n",
    "        "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "print(matplotlib.get_cachedir())\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.rc('font',family='Times New Roman')\n",
    "def add_value_labels(ax, spacing, labels, specials=None):\n",
    "\n",
    "    for rectid, rect in enumerate(ax.patches):\n",
    "        x_value = rect.get_width()\n",
    "        y_value = rect.get_y() + rect.get_height() / 2\n",
    "        if specials is not None:\n",
    "            space = spacing +specials[rectid]\n",
    "        else:\n",
    "            space = spacing\n",
    "        ax.annotate(\n",
    "                labels[rectid],                      # Use `label` as label\n",
    "                (x_value, y_value),         # Place label at end of the bar\n",
    "                xytext=(space, 0),          # Vertically shift label by `space`\n",
    "                textcoords=\"offset points\", # Interpret `xytext` as offset in points\n",
    "                ha='left',                # Horizontally center label\n",
    "                size=12,\n",
    "                va='center') \n",
    "\n",
    "# sns.color_palette(\"light:#5A9\", as_cmap=True)\n",
    "f, (ax1, ax2) = plt.subplots(ncols=2, nrows=1)\n",
    "plt.sca(ax1)\n",
    "ax = sns.barplot(y=sorted_wordss[0][0][:15], x=np.array(sorted_wordss[0][1][:15]), ax=ax1, color='#60aaa2')\n",
    "add_value_labels(ax1, spacing=5, labels=sorted_wordss[0][0][:15], specials=[-65]+[0]*14)\n",
    "plt.yticks([])\n",
    "# plt.xlabel(r'')\n",
    "plt.xlabel('Frequency')\n",
    "\n",
    "plt.sca(ax2)\n",
    "ax = sns.barplot(y=sorted_wordss[1][0][:15], x=np.array(sorted_wordss[1][1][:15]), ax=ax2, color='#60aaa2')\n",
    "add_value_labels(ax2, spacing=5, labels=sorted_wordss[1][0][:15], specials=[-40,-20,-20]+[0]*12)\n",
    "plt.yticks([])\n",
    "plt.xlabel('Frequency')\n",
    "\n",
    "# plt.xlabel(r'Label: SPORTS')\n",
    "# ax.set_ylim((0,10000))\n",
    "# ax.set_yscale('log', base=10)\n",
    "# plt.show()\n",
    "plt.savefig(\"../plots/toplabelwords.pdf\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/home/hushengding/.cache/matplotlib\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ],
      "image/png": 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eIlgSOjq6L/1cNie/xXffEjhy5BBnzpyid+8P2Lt3N02bNmfu3O9Ys2YF9es3wMPjMkuW/EpycjJr166gYcNGXLhwjk8+mV6gHDe302zZsoHDh13Iy8uTJ4pdvnyRefO+R0dHh02b1mFhUYOcnBwuXnTniy9mEx4ezpkzLnTr5sSUKfllHjiwB4CrV//h66/nI5Hk4ey8jF69+nD9uieRkZFs3ryDR48eEh0dyYUL59DS0iI7OxsPj8uAGjo6Okyf/jlubqfZvn0zY8ZMYMeOrTg59ebw4QPs2PEXtrb12bt3N0lJCcyYMetdf/SCIJfwJIzE0DAAIm7cQk1DHUlODhnxiRjWsKB2y+YARN66U+jxGzduEB8fz969u3Fy6gWAv78fv/++DE1NDRYv/hVb23rs3/8XWVlZBAcH0bt3H7p27VFu+yOOfEugVas2REaG065dR3bu3Iuvrw9XrlzCwKAKw4ePwsSkKt7eXlhZ1UVPT5+srCzWr99Kq1atC5TTtWt3YmNjAAokijVq5MDFi+6Ym1fHzMyc6OgoZsz4gmnTZvLHH9sYPHgo69Zt4eDBfQBcu3YViUTKiBFj6NbNifXrV2FrWx+JRIpMJuW331Zhbm6Or683zZo5Ur26BT169KJp0+acPHmMvn0HMH36TA4e3AtAly7diYyMxMGhKStXruezz2ZRo0ZNNDTy/zZLJBImTHh1ILwglLdq1laYWNbBxLIOhjWqkxwZjVXb1jT4oCePr14jOSIy/78iHu/Xrx8Ao0ePk9+sQF9fj337DtO0aXNWrfodgBMnjjJhwif07z+I48ePlOv+iMG3BLS0tNDT06dOHUuMjIzp3Lkbjx6FMGbMx7i4HOfZs6fk/nvtoK6uLvXq1cfU1AxjY5MC5bz49eXFRLHw8FB5opiOjg6WllZoaGhgZmaOoaERVapU+TdxKQMAX18fEhLicXU9SUZGOqamZkD+Vykbm/y8XzMzczIyMgrty6JFy8jMzMTd3a1AnQHq1auPrW091NXVGTjwI44e/RuAzMyMYkJ9BEExngY+QM/YCAB1dXVMbW14FhxS7ONFsbGph5qaGkOHjiQw8D4Au3Yd4PLlC9y+fbPcU/7E4PsGjIyM0NPTY9u2jfTp0586dSxLXUZkZAS//baEHj16yaMlS0oikVCnjiV9+w5g7NgJzJ49r8jtijopsHnzelJSkunX78NXvke/fgO4cOEc3t5eNG1autu0CEJ5k8lk5KT/d3Chpa+Hmrp6sY+/iq6uLiYmJuTl5fHdd3No2LAxbdu2L7e6PycG3xLKysqSD2ahoY8JDLwvH3QTEhKQSiXyI82SnNx6MVEsPj5OnihWEo6OLdm1awd3794mISGekyePvXL7F1PKDh8+SKNGTYiLewYgv3fcy/U2MjKmffuObNy4ltat25WoXoJQ7mQyZDIZZnb1iXv4SJ7Rm5WUTHX7BsU+DvlHwrm5uYXS+vz979Gr1wc8fBhMZGQ4FhYWxMWVrk++CXHCrYRyc3PYv38PGhoatGrVhjp1rPj55x+JiYnG0tKSK1cuY2/vwMOHD0hPT6djxy7o6enJE8giIiK4efMeANeuedCqVWt5opiVVV18fLwYMGAQ/v5+aGio8/RpLN7eXkRFRfL48SMiIvJPNHh4XKFr1+74+99l7tyvqF27DosWLSMsLJTIyAhu3PCmenULnjx5hJqaGt26OdG6dVs2b17PN9/Mp0ePnnzxxVRGjBiDpaUV58+fpWpVUwBcXU/Qv/8g+T4PHjyUM2dcxTXJglLITEom4UkYqIFFo4bUcmyK/zEXjGrXxLSeDYYW1QGKfbxduw4sXDiPefO+p3//gezf/xeBgffJzs5i4sQp5OXloaWlzcyZn9Kr1wc8e/aUR48eyg9QPDyu0LChPSEhwaipqdGlS/e3usZapJq9oLi0o+joKD7/fCp//33yjctW9mWURdXP29sLIyMj7O0bl+t7i1QzobyVZZLZi0SqmVCmYmJiePgwmBs3vMt94BWEykoMvq8hkUi4dOkCiYkJ3LlzW9HVeSfOnj3F/PmzxQ00BaEclWraoaIr62yHF6nitMO7ogzTDkLFVl59+22mHUSkpCCg3G37VVS93at6/d+GmHYQBEFQgAodKVme0whlzdl5GW3atCvXteRC8ZS9bb+KqkdKGpnolrqf5uTksHv3HwQF3ef331cXeO7FCEhlVqEjJcs6IrKsubgck19XO378JIyMjBVboUpM1dp2RVKafpqWloaPjxfdu/fEwaEpt275FtrmxQjI4kgkEs6edaVv3wFvUuUyIaYdFMTHx4tz587Kf7awqIGenp4CayQIyk0mk7F6tTOZmZlA8bGQL0dAFmXHjq1ER0eVaf1Kq9IuXTpx4igZGemEh4fRoIE9AwcO4dixw+Tk5ODtfY0RI8bQunVbbt68gb+/H0+ehFCvnh1du/bg++/nMmzYKNq378TSpT/SqJEDXbp0Y9UqZ5ycenH27GlSUlJYtsxZHlOnoSHj9u279O7dhw4dOnP16j9ER0exd+9uOnXqzNq1K3Fy6k2fPv159uwpR44cwsjImPv3/Zk162sAtm7diLGxMWlpaVy7dpWlS3+nceMmCv4kBaF8pKenFYhtnThxCk+ePCY3N4dq1Uzlg+9ff+3kxImjjB49jkGDhhYqx8PjCmFhody6dYP+/QfSsGFj+Q0Ozp93w8mpd6GI1rp1rTlwYA9aWtocPLiPDRu2ygOsykqlPPL187uLv/89Ro4cy1dfzeXPP7eze/cfhIY+YfjwUQwbNgpPTw9SUpL5449tjBs3gRkzZnHt2lVq164jD8KpWrUqjRo5AGBn1xCpVEpeXh5bt+7E0bEFGzasAfIH+hkzZshj6rS0tOjSpTvVq1swevQ4rKys0dc3kGdH/PLLYgYO/IhRo8bSsmUrli79CVNTM0xMqhITE8M333zL4MFDcXM7rZgPUBDegbt37xSIbY2Le4aNjS1t23agXbsOAMTHxzFw4EcsWLCoyAjIxMREzp07zejR45gxYxa//voz1apVo0mTZjRp0gwnp95FRrQmJiYSEODHkCHDmD17bqnufFFSlXLwvXjxPDVr1gJAU1OTtm07kJiYQN261gC0bdueWbO+5t69u1Srlp97YGpqxrp1W15ZrqamJnZ2DQHo1+9DgoICgPyYOjc3t1fG1D1Px09PT8PX14caNfIzRzt16sKNG95kZ2ehra1N3brWqKurFxsZKQgVRfv2HYuMbX2RqakZhoaGmJubk5aWXuj5gAA/MjMzcXU9ib//XRwdW5CamlJgm6IiWo2MjIiMjGTWrOkYGRnJx4GyVEmnHWQkJMTLfzIxMSE7O4uIiHD5YwkJ8UilkgKPZWRkoK6uXqK/gkZGxujrG8hj6pYuXYxMpoW//71X10yWfzIgKSkJExMTjI1NUFNTQ02t8N9JsT5GqMj8/f04ceIIc+cu4M6dW6/cVk1NDZmscJqgRCJBT09ffmKtX7+BSP5NPHtxm7p1reXb5OTkoKamxoYNW9m7dzczZ37KihXrad7csWx27F+V8si3Y8cuXLt2Vf6XNCoqEkNDI1xcjhMYGEBCQjweHldo3LgJjx8/wtX1JJmZmRw/fgRdXV309fWJi3tGXl4eYWFPyM7OlpedlZV/1vbx4xA6duwij6mrWbNmgZi65zGPWVlZBY6Gq1SpgqNjSy5fvgDk3/yyc+dupbrnlCBUBG5uroViW4uLhSxO48YOeHp6cPasKykpyRw5cpC8vLwCMatFRbTGxsZw795dJk6cwscff0JgoH+Z71+lPPJt2bIV/fsPZNGi72ncuAnt2nWgT5/+pKWl8eWXM7C3b8zixb9gZGTM/PkL2bBhFXv2/MnChUsA6NNnAHPnziIiIhwLi5rExz+T5+NeuHCOpKREgoMfMHnyVDQ0NNDS0mbs2LF069ZLHlNnY2NLYmICO3ZsYejQkfKYuu7dezJ//kKcnX8hMTGB7Oxsvv56HsnJSfK4yaioSO7cuUVIyENiYmLkUxSCUJG0b9+pUGxr9+5O7N79B+bm1bl9+yZRUZGEhj7h5s0bJCUlEhgYQGJiIpB/oq1Tpy7Mm7eADRvWsGXLBr755lt0dXVxdGzJzz//iI2NLb179ykU0QqwbNkiRo8eR2pqCmPGjC/z/avQkZLlFSNXnJkzP2XSpE9p2bJVoeeUfRllZc92ULW2XZG8635alkSkpCAIgoop1bTDzx+NLK96lIvsIs6OlpegoEAiIyPw8rqKg0NT+dULgmpQtbZdkbzLfqpMRKpZGWnY0J6jR10V9v7C21Hmtv0qim73b8vc3FBl8lfKmph2EARBUIAKk2qWK8l75fMHDuwhISGB27dv4uy8+o1CbNzczhRY7itUHMrctl9H2VPNiksXPHRoP+fPn2Hz5p1l8j4PHgTi7PwL06d/XuRJ75e9mH4WGRnBmjXL6d69J3369C+T+rxOhUk1y09GKpqnpwfx8fHMmPEF58+fA95sqWCnTp1p0aLlG9ZQUGbK3LZVXXGpZW3atOPvv/eV2fs0aGBfqmXAL6af1a5dB11dvXe6cKlSXOcbHBwkPwHm5NTrjcvR1zdAX9+grKolCJVaeSwcKk2ZL6efveuT5BV+8L158wY3b+Znfh45cghNTc1CaWZubqfZunUj48dPYseOrRw4cJTz58+RmpqCp+dVJk/+FGtrW3bs2EJubi5z5nxHXl4eq1c706xZC06ePErVqtUYPHgo165dJTk5CWNjY9zd3fjqqzl07txNsR+CICixHTu2cvjwAdq168Ds2fMKJJktWfIrxsYmuLgcIzc3D1fXE8yaNYcmTZri6nqyQB9t1swRyF+W/Pvvy9DU1GDx4l/lyYJZWVkEBwfRu3cfpbhpQYU/4dayZSuaNWtOs2bNadCgYaE0Mz+/u3Tp0p2YmGgcHJqycuV6IiMjefAgiBEjxjBy5Gh+/30ZRkZG1K1rI18K7OZ2mtTUVHr3/oD33muNpqYmLVq8R61atQgPD2Py5GlMm/Y5Li7HFfwJCILySk9Pp2/fAezadYCbN325cuVSgSQzb28vANzd3Rg4cAgLFy5BTU2NR49CCvXR5/T19di37zBNmzZn1arfgfxkwQkTPpEnCyqDCj/4vqioNDMPjyvo6uZ//ahXrz62tvW4desG6elpuLqeJDIyAktLKyQSSYGvJVlZWWhpaQH5QehpafnzhdraOtSuXQcdHR3MzMxE8pggvIKBgQE1atTE1NSMHj16ERgYUGSSmb6+AVOmfExsbAwODk2K7aMANjb1UFNTY+jQkQQG3gfykwUvX77wymTBd63CTzsUVDjNTENDo9BWEokEU1MzecrRwIEfoa5e8O9U374DePjwATKZjLi4ONq2bVf0O4rkMUEoET09PdTVDdi2bWOhJLNFi5Zx8uQxFi6cxzfffFuiPqqrq4uJiYk8WXD27HkYGhq9NlnwXak0R74ymazINLNu3Zzk20il+ZF0zZu35Nixv7l2zYPk5CSOHDlY6Czq06exhIY+4dSpE5iZmTFkyPB3tzOCUEG8eHDy4EEgd+/eLpRklpGRwdmzpxgyZBjffPMtfn73StRH/f3v0avXB/JkQQsLiwLJgopW4Y98o6OjuHHDW54Y9nKaWf36dly+fBEAV9cT9O8/iIYN7Zk4cQpLl/6EkZEx33+/iMzMTG7fvsmjRyHExsaQmZlJaOgTVqz4FZlMxqFD+1i2bDm3bvny6FEIkZER+Phcl6cumZs3VfAnIQjKpWrVarRr147ff19KzZq1+PDDwWhqahWRZNaTDRvWkpycTHp6OkOHjqBOHctCfRSgf/+B7N//F4GB98nOzmLixCnk5eWhpaXNzJmf0qvXB/JkwdTU/JWBHh5XaNjQXp4s2KVLd6pUKf/rvitMqtm7Tka6dOk8DRrYU6tWbaRSKU+ePCY09DHdu/cscntlXwYqUs2Ut22rulf1TWXvF68jUs0UYM+eXfKvTOrq6kRGhtO0aXMF10oQBFVR4acdysukSVNYuHAeGhqa1K5dh8GDh2FmZq7oagmCoCJKNe2gzHIleSQlZCq6GsVS9q9XlX3aQSg/xWU7gPL3i9d5m2mHChUpKQhvSpnb9quo+uBVmYk5X0EQBAVQ2UjJV32VgfxMhxUrfmX58rXyVW0vu3HDm6Cg+1y44M7EiZ/QqVPX8qquoOSUqW2XljJ+63td/yxKTk4Ou3f/QVDQfX7/fXWB516MfyypCRNGs3z5GqU9F6OykZLFxdQ917JlK/l1fEWRSCSsW7eSP//cT+fOXYmLiyuHWgqqQpnadkXwuv75orS0NHx8vOjevScODk25dcu30DYvxj8WRyKRcPasq3zV26JFyzA1NSt13d+VCn21w6vi5RIS4klPTwfAysoaKyvrd1QrQRCek8lkrF7tLA8/L67Pvhz/WJQdO7YWWGJct651mdSxvFS4wdfF5RhJSUmkpCSTmpoCUCh6zsrKmkOH9pOWlsbu3Tvp1et9cnKycXd349mzpxgbmzBp0qccOLCXe/fu0LRpM06ePMbIkWMZMmQY2dnZ7Nu3G11dXa5d8+S7737AwqJGsRF3giDkS09PKxAZ+dVXs3jy5DG5uTlUq2YqH3z/+msnJ04cZfTocQwaNLRQOR4eVwgLC+XWrRv07z+Qhg0bExDgB8D5824YGhqxdu0KnJ3XULNmLW7evIG/vx9PnoRQr54do0ePf6f7XZQKdcLt8eNHXL36D2PHTuDTTz9DKpWRmZlZKHrOxMSEwYOHUqVKFcaNm0CNGjVZt24VY8dO4Ouv53PmzClCQoKxs2tAeHjov1F2izl+/DCQ3zBsbeszcuRYWrRoyZ07t18ZcScIQr67d+8UiIx8+vQpNja2tG3bgXbtOgAQHx/HwIEfsWDBoiLjHxMTEzl37jSjR49jxoxZ/Prrz1SrVo0mTZrRpEkznJx606ZNO5KTkwFISUnmjz+2MW7cBGbMmMW1a1ff6T4Xp0Id+Z4/70b9+g2A/MhIY2PjAtFzGRnpBaLnnsvIyCA8PAx397NA/u1N0tLS0NbWxtTUDCMjY8zMzOVhHJ6eHvTq9T4AEybk377o8OEDRb5PUalpglBZtW/fETu7hvLIyKLiHU1NzTA0NMTc3Jy0tPRCzwcE+JGZmYmr60mkUgmOji3k33Jf9DwC9t69u1SrZiove926LWW8V2+mQg2+KSnJ8ozd50oSPSeRSMjOzuaDD/qhrq5O374DyMnJwc/vboHtnq9HkUolhIeHy+eJExLiS/Q+glDZ+fv7ceLEkUKRkUVRU1NDJpMWelwikaCnpy/va/36DSx0QPUiqVRCRES4/OeMjAzU1dXlOd6KUqFGhyZNmnPp0gUyMtKRSCTk5uZib9+4yOg5mUwmj5A0NDTEyMiYDRtWk5iYwPXr13j06GGx7+Po+B47dmwlISGeoKBAgoLulyjiThAqOzc31wKRkRKJBHV1dXJzc0lJSS5RGY0bO+Dp6cHZs66kpCRz5MhB8vLy0NDQIC8vr1A5jRs34fHjR7i6niQzM5Pjx48ofOCFCjb49uzZm/btOzJ9+mR27vwfxsbGBAbel0fPTZ8+maZNm5OTk4Ob22ni4+P4559L5ObmsnDhYnx9bzBq1BDu3/fHzq4h3t5eREVFEhb2BC8vT5KSEgkI8GPixClUq1aNUaOG4Op6gnbtOhaIoXz+PoIgFNS+fScOHNjLypW/YWlpyfnz53F0bMmBA3vw87uHl5enPIb1eZ8LDAyQz9N6eFzBzMycefMWsHnzeiZMGE3NmrXR1dXF0bEl586dwcvLk8DAABITE7h27SqmpmbMn7+QLVvW88kn42jR4j0Ffwr5VDZS8uWYOmVfZinqVzxlyHZQprZdEZQ04lXZ+8XriEhJQRAEFVOqE24/fzSyvOpRatn/3gpIEMqCMrXtikD0z9erMKlmgvA2VLVtq/rX9spMTDsIgiAogEqmmr1JYlJp+fr6sGLFr6xYsa5AKlp8fBwbN67F0tKKCRM+ITo6ih9+mM+2bbvKtT5C+VKWtv0mlC3V7F30z+cOHdrPmTOn2L599zt5v7KkkqlmpUlMelPvvdeatLTC+2pqaoapqZn8om4LixosXLi4XOsilD9ladsVwbvon8+1adOOAwf2vJP3Kmti2uEVik9Y0pH/W11dXSSiCYKCvCq5UNlVmOXFJ06cYMWKlYwfP4kdO7by8ceTycnJlieM6enpsWqVM05OvTh79jQpKSksW+aMVCplzpxZLFy4mFq16vDdd98wdOgI+dJFD48r7Nu3G3Pz6vz882+Ym1cv8L4HDuzB3f2sfNrh2LHD5OTk4O19jREjxtC6ddt3/lkIgrJ6uX+YmRlx+fJVJBIJKSnJfP75bDQ1NVm/fjXm5uZ4e19nwoT8RUspKSkcOrQPHR0dbt70ZfHiX+Tlurgc48CBvXTq1JWpUz9T4B6WXIU58u3VqxcxMdE4ODRl5cr1hIY+KZAwZmfXEKlUSl5eHlu37sTRsQUbNqyhfn07ateuA0CNGjWoV69+gXJr1arNwYPHqVLFkG3bNhV637ZtO5CYmAjk3z0jNPQJw4ePYtiwUXh6epT/jguCini5f5w/74azszMffzyZyZOnkpSUxL59u0lPT+P27ZuMGDGGTp26cPp0/t0r1q5dQZcu3Rg7dgI1atTg4cMHAGRmZtCmTXtWrFjH0aN/K3IXS6XCDL56enoA1KtXv0CSWWRkhDxhTFNTEzu7hgD06/chQUEBry3X1rYempqaDB48lMDAwtu/OAXh6ekhD3Bu27Y9s2Z9XQZ7JggVw8v9o1Onrujr68szUDp27IKHxxUMDKqwYcNW3NzOEBR0n9x/rxm+ds2DunVtAJg7dwGOji0B0NPTp3p1C8zNq5OuQvP2FWbwfdGLCWNDh45k8eJfCiWMGRkZo69vAFCiABw9PT2MjU1euc3L6UkJCfGlr7wgVFAv94/U1BTi4//rIyYmJmhqapKSksLChfNp165DgRsSSCRSIiLC5D+/3L+eB2apigo3+Eql0lcmjGVl5Z+Fffw4hI4duwCgr69PXFwcWVlZREVFkp2dLS/v+S/Tz+8uvXv3eeV7Ozq+h4vLcQIDA0hIiMfD40p57KIgqKSX+0diYgKxsbGEhT0BICIinB49enHzpg95eXkYGRkRF/cMqVRCRkYGLVq0ZPPm9aSmpuLt7UVsbIxid+gtVZgTbufOnQPA1fUE/fsPkieMGRkZ8/33i+TbXbhwjqSkRIKDHzB58lQgP3v3t9+WEBjoT506loSFhZKamsqQIcNZv341zZo1R0tLmwEDBpGSkkxAgD9qavl/ea9du0pSUiL37/vTpUs37ty5xZdfzsDevnGBEwKCUNkV1T8cHZuwapUzrVu3Iycnm3HjJpKQEM/69auZO/crWrZ8j6CgIJ49e8qsWd+waNEChg37kDFjxjNu3ET+/ns/yclJBAT4ye9cce2aB+3bd1Lw3r6eSqaaFZWYVJJlljNnfsqkSZ/Kb9b3Lin7MlCRaqYcbbsiKGmiGSh/v3gdkWomCIKgYirN4BsUFEhkZAReXlcLzOkKgiAoQqmmHZRFUWvHlf3ri6hf8ZRh2kEoO6XJdlD2fvE6bzPtICIlBQHVbduqPnhVZpVm2kEQBEGZiEjJIhw4sIeEhARu376Js/NqjIyMy+29BOWgLG37TSg6UrK0/TEnJ4fdu/8gKOg+f/yxvcBzFy64s2HDag4fdilxeRMmjGb58jWYmZmX+DXKQERKvsTT04P4+HhmzPiC8+fPAeL275WBsrRtVVTS/piWloaPjxfdu/fEwaEpt275FtqmY8dO/PDD/FeWI5FIOHvWVR5+tWjRMkxNzd6o7ookph1eEhwcJM9rcHLqhZGRkYJrJAiqTyaTsXq1M5mZmcCr4lp1X1vWjh1biY6Okv9ct651iSIClE2FWeG2atUq9u8/wK5d+zl//hybNq1ly5Y/sLKqy4IFc2natDmJiQmAGjo6Okyf/jlubqfZunWjPIZy3rzvuXkz/6/xkSOHeP/9PqxZs4L69Rvg4XGZJUt+JTk5mbVrV9CwYSMuXDjHJ59Mx87ODnd3N549e4qxsQnTps1U7IchCAqWnp5WoO9MnDiFJ08ek5ubQ7VqpvLBd+vWrezbt5/Ro8cxaNDQQuV4eFwhLCyUW7du0L//QBo2bExAgB8A58+7YWhoxNq1K3B2XkPNmrW4efMG/v5+PHkSQr16dowePZ6LF91JSkrin38u07dvf3r2fP+dfhbFqTBHvjNnzkRDQx0NDQ2GDRuJiUlV9PT00dHR5b332vDoUQh9+w5g+vSZHDy4F4AuXboXiKFs374jzZo1p1mz5gwZMoy7d+9gYFCF4cNHYWJSFW9vL6ys6qKnp09WVhbr12+lVavWrFu3irFjJ/D11/M5c+YU9+/7K/jTEATFernvxMU9w8bGlrZtO9CuXQcg/5ZcI0eOZMGCRRw/fqRQGYmJiZw7d5rRo8cxY8Ysfv31Z6pVq0aTJs1o0qQZTk69adOmnXxZcUpKMn/8sY1x4yYwY8Ysrl27CsCpUyfo1+9Dliz5BV3d1x9ZvysV5shXS0uLzp27c+nSBdq164CZmTlnz7rStWsP6te3Y9Sosdy5cwt3dzd5RN3zX8TLGb7PtW/fETu7hri4HOfZs6cFXlevXn1MTc3IyMggPDwMd/ezQP5tTYq6/ZAgVCbF9Z0XmZqaYWRkhLm5OWlp6YWeDwjwIzMzE1fXk0ilEhwdW5CamlJou+fThPfu3aVaNVN52evWbQHypyXGjx/JlCnTcXLqVZa7+VYqzOAL0Lv3B2zbtom8vFy+//4n5sz5EkNDQ4YOHcnmzetxcGhCv34f8ssvJbvnmr+/HydOHGHu3AXcuXOryG0kEgnZ2dl88EE/1NXV6dt3ADk5OWW5W4KgckrSd57Lj4KUFnpcIpGgp6cvP7HWr99A+b0Ti/JyZGVGRgbq6upMn/4FjRs3Ye3aFURFRTJu3IQ326kyVmGmHQCaNm0uj5mzsrKmWrVqJCcno66uzuHDB2nUqAlxcc8ASE3978J0qbTgL/75oj83N1fq1LEEICEhQR5t9+JrDA0NMTIyZsOG1SQmJnD9+jUePXpYvjsqCEquqL6jrq5Obm4uKSnJJSqjcWMHPD09OHvWlZSUZI4cOUheXh4aGhrk5eUVKqdx4yY8fvwIV9eTZGZmcvz4EXR1dTlzxgUnp94sXeqMv//dMt/XN1WhBl81NTU++KAf3bo5AdCrVx+6du0OQI8ePfnii6lcvfoPlpZWnD9/lsuXLwL5MZQA0dFR3Ljhzc2bNwgJeUj79p04cGAvK1f+hqWlJVeuXCYqKpKHDx/g6ekhv33QwoWL8fW9wahRQ7h/3x97+8YK2HtBUB5F9R1Hx5YcOLAHP797eHl5EhUVSUhICF5eniQlJRIYGCCfp/XwuIKZmTnz5i1g8+b1TJgwmpo1a6Orq4ujY0vOnTuDl5cngYEBJCYmcO3aVUxNzZg/fyFbtqznk0/G0aLFewDs2/cX27dv4caN64wbN0mRH0sBlSpSUpFE/YqnDNkOytK2VVFpIiRfpuz94nVEpKQgCIKKKdUJt58/Glle9SiV7CLOnArC21CWtq2KRH98MyLVTBBQ3bat6l/bKzMx7SAIgqAAKpVqVhZpZsHBQSxf/itTp35Gy5atcHM7w/37/sya9XWhbZ2dl9GmTTu6du1R4vIjIyNYs2Y53bv3pE+f/m9VV+HdUXTbfhuKSDUr72TBykClUs3KIs3Mzq5hget6O3XqTIsWLeU/u7gco3//QQCMHz+p1HGStWvXQVdXDxW8QUilpui2rWrKM1nwZS/2yYqkUk47PF+OCKCvb4C5eXUAfHy8OHfurPw5C4sa6OnpvVX5giC8OReXY9y5c1vR1SgXFWJ58ZEjhzh//gzdu/di797dNG3anB9//BkXl+NkZKQTHh5Ggwb2DBw4pMDrUlJS2LFjC7m5uXz55RyuXv2H6Ogo9u7dTadOnVm7diVOTr3p06c/MTExnDp1HIlEQmRkOAsXLuHx40ccOXIIMzMzoqIiWLhwiYI+AUFQDunpaRw6tB8tLS1OnDhK8+YtMDGpSlRUJHfu3OLLL7/Byak3aWlp7Nu3m5o1zbl+/QZTpkzDxKQaO3ZsIT09nfj4eCwtLUlKSiIyMoITJ47i5NRLXvaFC+5s375b0bv7VirE4NuqVRt27NhCu3Yd6d27D6NHD2Xnzv8RGxvDt9/+QF5eHsOHD6Revfo0adJM/jojIyPq1rUhIMAPLS0tunTpzsOHwYwePQ7IPyp+Pn3wyy+LWLToF0xMTJg79ytiYqJxdz9L+/Yd6dKlGwMG9CYuLg4zM9ULdRaEsnL9uhcmJiYMGjQUU1MzQkOfEBb2hEWLfuH8eTeWL/+VTp26snHjGnr37kOvXl0xN6/Nd9/NYdeuA1hZWXP6tAtr1mwkMTGRO3duceuWLx9+OJgLF9wLlK3qKsS0g5aWFgYGBtSpY4mRkTGdO3cjLS2VmjVrAaCpqUnbth3w8LhS6LWvmiJ4/lxGRjpPnjzCxMQEgN9/X0WdOpZMn/45NWvWxMXlOFKphNxcEagjVG729o3Yvn0rv/32M61atUVTUxNb2/poamrSq9cHZGdnERkZwaVLF+T9s02bdkRGRhAREYauri7W1jbo6xtQu3adV5at6irE4PsyIyMjDh7cR0JCvPwxExMTNDXf7EBfKpWRlJRUIIwnISGeo0f/5s6dW/TvPxA9Pf23rrcgqDoLixrs3LmXzMxMJk8eUyBeVV1dnSpVDDEwyP9GmX9zg/xMFiMjYzQ0Xt0/Xy77xf6oiirM4JuZmSmfIggNfcxnn33JtWtX5TmiUVGR8sCd4jxPS8rKyioQC1mlShVsbOqxadNaMjIycHM7Q3Z2NkePHsLevjFpaWlkZWWRmZnxysg7Qajorly5iK6uLj/9tJR69RoQHR1JVlb+VRGJiYmYm1enenULOnbsLA+2SktLo2bNWvIj3RevRvovCS2lyLJVWYUZfHNycti/fw8HD+6jVas2jBo1lv79B7Jo0ffs3bubdu06UL++HeHhYURGRuDjc520tDRu377Jo0chxMbGYGNjS2JiAjt2bCEpKZGQkGBu375JZmYmCxb8hJ/fXYYP/xCJJI+aNWvRtWsPfvzxO44ePYS1tQ3u7m4kJMTLXydC1YXKJjMzkzlzvuT48SM0buyAnV1D7t27g4vLcfbu3cV33/2ImpoaM2d+RXBwEKtWrWLPnj9ZsOAn0tPT8PX1/vc2QI+B/JjIu3dv4+p6osiyVZlKpZoVl54UHR3Fl19O58CB4wqoVcko+zJQkWomrvMtjZImmW3fnn83icmTpxb5vLL3i9cRqWaCIAgqRuUHX4lEwqVLF4iPj6+wF2MLgip6+jQWf/97+PndJT4+TtHVUTqlmnZQtFetJ1f2ry+ifsVThmkHoXTKKttB2fvF67zNtIOIlBQEVLdtq/rgVZmp/LSDIAiCKqp0kZIv2r37D7S1tRkxYkyZlPdyXKWgOhTdtt/Gu46ULK4fHjq0n7NnXfnf/3aVuszLly+wevVyjh51LdXrUlNT2b59C7m5OcyZ891rt38x8rVnz/f566+dBAYG8Ntvq0pd57dV6SIlX9S//yDU1dXKrLyX4yoF1aHotq1KiuuHbdq048CBPW9UZvv2nViwYG6Jt09LS8PHx4vu3XtibW2Nv79fiV73YuSrlpYW9vaN8fX1eaM6v61KPe1QtWpVjI1NyrRMEScpVFba2trv5LUymYzVq53JzMz897Wl63Mv9lFF9leVTzVzczvN1q0b+eyzGaxZs5aPP55MTk42np5XmTz5U5o1cyQ4+AGnT5/EwqIGZ8+60qyZIyNHjmXr1o1YWdVlwoRP5BF3hoaG+Pv7MWXKNKpVM2PXrh0kJydhbGyMu7sbX301h86du3HjhjceHpcBNXR0dJg+/XNFfxSCoBR27NjK4cMHaNeuA7Nnz2PNmhXUr98AD4/LLFnyK8bGJri4HCM3Nw83t1N89tlsmjRpKn/9jRve7NixlSlTptOixXu4up4kNTVF3qerVKnCkyePyc3NoVo1UwCys7P5/vt5+Pr6MHHiJwwfPprg4AdKHfmq8ke+Xbp0JyYmmubNm7Ny5XpCQ58wYsQYRo4cze+/LwNgzZrltG3bgREj8oM+OnbsQo0aNTEzM5dnMWzcuIbWrdsycmT+suTvvpuDvr4+tWrVIjw8jMmTpzFt2ue4uOSvojt58hh9+w5g+vSZHDy4V2H7LwjKJCMjg759B7Br1wFu3vTlypVLGBhUYfjwUZiYVMXb2wsAd3c3Bg4cgrOzM2pq/039paamcuOGN6tXb6RFi/d49CiEBw+CCvRpW9v62NjY0rZtB9q16wBAfHwc33+/iF9+Wc6GDWuIjIyQR75OnjwVb+/rxMUp17XGKj/46urqAtCwYUNu3bpBenoarq4niYyMwNLSColEQnZ2FlpaWgDUqFGT9H/n9l78ylFcxJ22tg61a9dBR0cHMzMzMjIyAFi0aBmZmZm4u7vJw3sEobLT19enRo2amJqa0aNHLwIDAxgz5mNcXI7z7NlTeV/R1zdgypSPiY6OxsGhifz13333De+/31c+DVFcn35ZrVq10dXVxdGxJfXq2REcHKT0ka8qP/i+SCKRYGpqRt++Axg6dCSLF/+Curo6s2fP4+HDYCB/or5ZM8dCry1pxN3zNSmbN68nJSWZfv0+LL8dEgQVpqenh7GxCdu2baRPn/7UqWMpf27RomX06/chX3zxBe7u/926q3v3njg7L5OfuC6uT5fkfZU98rXCDL5SqZTmzVty7NjfXLvmQXJyEkeOHERNTY0bN7wJCQnm7FlXZs+eJ58netGrIu6KcvjwQRo1akJc3DMAlc8WFYSy8OKC2QcPArl797Z80E1ISEAqlZCRkcHZs6cYMmQYixYtws/vnvw1gwcPRU9Pn127dgAU26f/i5pMLvC+mZmZ5ORk07ixg9JHvqr8CbfnA+bhw4fp1u0DJk6cwtKlP2FkZMz33y8C8gfGy5cvcubMKbS0tBg8eBhjx35MQIA/6upqJCUlMXPmV/z88w9s2bIBgAULfiIzM5Nbt3x59ChEHkMZFRVJaOgTevToyRdfTGXEiDFYWlpx/vxZ3nuvjXy7Jk2avdXZX0FQNVWrVuO991rz++9LqVmzFh9+OBhNTS1+/vlHYmKisbS05MqVy3Tv3pMNG9aSnJyMTJbL0KEjuHjRHQBPTw8++mg43377NYaGhnz44ZAi+/R777Vh585tWFjUoFkzRy5dOs+2bZuQyWQsWLAIHR1deeTroEEfySNfBw8eSkhIMGpqanTp0l3epx8/foSNje07/bwqRKQkvHqZ5d69uxg9ejwAubm57N27i48/nlxu9SyKsi8DrezZDopu26qkpHGSJaHs/eJ1RKTkKwQHB3Hrlq98ol8mk1WIm+8JgqDaSjXt8PNHI8urHiWS/QZXFdjY1MPa2pYJE0ZRrZopdnYNmDSp6GBnofJSdNtWJW/SD4XCKnyqmaamJp99NovPPpul6KoISkwV2zao/tf2yqzCTzsIgiAoI5VINXvTNDM3tzPcv+/PrFlfF192djZjxw5n377Db3xreUH1KfqE39soz1SzkvS9+Pg4Nm5ci6WlFRMmfFLkNhER4axe7YyTU2/69OlfHlVVOSqRavamaWadOnWmRYuWr9xGR0cHZ+fVrxx4XVyO0b//oFK/v6A6xNUORStJ3zM1NcPU1OyV19DWqWOJgYEBKnTjnHJXoacd9PUNMDev/trtrK1tin3Ox8eLc+fOFvu8IAglSwcrbfpYRaeyg6+b22k++qg/Pj5ezJz5KWPGjOHBg0AWLJjD8OEDiYmJZvVqZ5ydl5GTk8Pu3TuZO/crdu/+g+HDB3LkyKEC5QDk5eXx55/bOXRoP0OHDiA3N5erV/8hOjqKvXt34+3txdixwzl8+CAffvg+J04cpVOnVly/fg2Aixfd+emnBUq1ikYQysOxY4c5eHAf33zzBT4+1+WPx8XFMXv252zfvoWMjAyWLVvE0qU/yZ8PDw9j0qSxDBnSj1u3fIH8fnP06N/Mnv15gaXGFZ3KDr5du3YnNjYGc3ML1q3bQlxcHA8eBLF0qTNt2rTj4sXz1K1rQ05ODtra2tjZNSA8PJSBA4ewcOFijh8/XKAcyL8mOCMjg2HDRvLJJ9PQ0tKiS5fuVK9uwejR42jTph3JyUlYWNRg/fot9O07gJYtW8m/SqWnp/PJJ9PQ0NBQ2OciCOXt5s0bhIY+YfjwUQwbNgpPTw/5c2ZmZvKgHH19fRwdC077SSR5/O9/u/jww8EsW5a/Wu3UqRP06/chS5b8Ig/KqgxUdvDV0cn/JVlb26Cmpkb16tWpVas2kL/MMTMzo8BXIW1tbUxNzTAyMsbMzFyeTva8HABLy7pcuODO99/PxcGhKUXR0dGhXr36WFlZo6mpyZAhwzh27G8g/xYlL4aHCEJF5OnpQd261gC0bdv+lSe0X2ZtbYu6ujrDh48iOjqKxMRE6ta1Zvz4kXh5XaNTp67lVGvlo7KD7+u8bmK/qOf19PT44489mJlVZ/LkcUREhL/2fTp16kpw8APu3LmNhYXFG9dXEFSFVCop0DcSEuILPP9iPm9xtLV10NbWpkqVKkyf/gVTpkxj7doV7N69s6yrq7Qq7OD7Ju7evU1SUiJffvkNvXq9T0jIQzQ0NMjLyyMrK4ucnPw80Bfv06apqcmAAYNYvPh7unfvqaiqC8I74+j4Hi4uxwkMDCAhIR4PjysFntfX15cHlz96FEJ2drb8uecHPQEBfnTr5oSWlhZnzrjg5NSbpUud8fe/++52RMFU9sLW579wT08PatasRWhoKD4+16lVqzYBAf5IJHnExsbw6FEIMTHReHt7ERUVSVjYE3x9b5CUlEhAgB/JyUkAXLvmgY6OLt999w1DhgzDwKAK7dp1IDs7i8TEBHbs2ELHjl1JSkrk7FlXPv54snxud8CAQQQHB5X5/eAEQRl16dKNO3du8eWXM7C3b8zixb+waNFC1NTyj4J79OjFtGmTWLBgDvb2Djx58ojw8DCcnHpz8OA+EhMTyMzMZPbseQDs2/cXsbGxaGhoMG7cJAXv3bujEqlmJUlRUuQyy7CwUEJCgl955Kvsy0BFqpm4zrcoZZlgVhRl7xevI1LNFCQxMZGgoEBcXU/SsWMXRVdHEAQVIgbft3D9uidz5syiRYv3RHC6IAilUqppB0UpyfpyZf/6IupXPGWYdhCK9qa5KiWl7P3idd5m2qHCR0oKQkmoattW9cGrMhPTDoIgCAqg1JGSxX3lmTNnFpMmfUqjRg7vtD7FmTBhNMuXr8HMzLxMyjt8+ACnT5/if//bVSblCa+nytMeZRUpWd5TDEJBSh0pWVyc3ZdfzsHCokaxr0tLS8PHx6vcFj1IJBLOnnWlb98BACxatKxM7wv33ntt2LfvrzIrT3g9calZ8f1NRKqWD5Wcdqhdu06x+bsymYzVq53JzMwst/ffsWMr0dFR8p/r1rUu0ZLKkipJPJ8gvAvPnj1l27ZNiq5GhaRyK9wCAwNYtcqZDz8czD//XMLBoSkhIQ+5f9+P1as3kZmZwZMnj8nNzaFaNVNatWrDnj1/oqmpyY0b3nz//SJCQh6ydu1KBg8eyp9/bmfJkt9wdl7K+PGTOHHiKDKZjLVrNyOTyVi58jdsbetx+fJF5s37Hk1NLQIC/AA4f94NQ0Mj1q5dgbPzGmrWrMWtW77cuXMLiURCSkoyn38+m7CwJ3z++W90796b69c9iYyMZPPmHVSpUoX161djbm6Ot/d1JkyYTNOmzRX6+QrCi+7cuUV8fDx79+6mUaPG3Lrly7NnTzE2NmHatJkEBz/gyJFDmJmZERUVwcKFS/D29mLt2hV88sk0Tp92ITIykp9+Wsq+fbu5dcuXZcuWY2/fSNG7pnAqd+Rrb98YqVRKzZq10NHRITk5mZ9+WkrHjh25fPkCtrb1sbGxpW3bDrRr14GTJ49hZVWXMWM+xs6uATt3bi8UDeng0ISUlBQMDKqwfv1W4uPjefgwmPDwMJKSkhg6dCSNGjlw8aI7NWrUoEmTZjRp0gwnp97/lpUMQEZGBhs3ruXjjyczefJUkpKS2LdvN7a29ZFKpchkUn77bRXm5ub4+nqTnp7G7ds3GTFiDJ06deH0aRcFf7qCUFDPnu8DMHr0OPbu3cXYsRP4+uv5nDlzivv3/XF3P0v79h2ZPHkq3t7XiYuLk/cJHR1dfvttFWZmZly6dJ6FCxczZMgwTp8+qeC9Ug4qN/jCf1/LtbV1sLWtB0D16tXlMZEv8vX1JiIiAlfXk+jo6FKlShV5GS9GQ2pra8vLMjMzIyMjHRsbW7799gdOn3YhPDxUHqxTXH1u3ryBvr6+fAqiY8cu8gwKHR0dbGyel58faWlgUIUNG7bi5naGoKD75IpbcgtKKiMjg/DwMNzdz+Lmdpo2bdqRlpbG9OmfU7NmTVxcjiOVSsjNze8jOjo68jvEmJv/F/darZppuU4JqhKVm3Z4laLWi0gkEho0aEjbtu0Bih1AiyorMjKCTZvWsnDhEqKiIkvyqgLxeiYmJq+cm05JSeHnn3/g++8Xk5eXK0/2FwRlI5FIyM7O5oMP+qGurk7fvgPIycnh6NG/kUjyGDp0JDt3/q9EZanAuq53QiWPfF9HXV2d3NxcUlKScXRsycaNa3j4MJjY2Bjc3Fzl270YDVmUK1cuYWRkjI6ODvHxcUilUjIyMuQxkykpyQW2b968Jc+ePSMs7AmQf8fWHj16FVv+zZs+5OXlYWRkRFzcM6RSSZFH74KgSOrq6ujq6mJoaMiGDatJTEzg+vVrPHr0kKNHD2Fv35i0tDSysrLIzMwQt9EqIZUbfMPCQomMjODMmVM8fPiAO3duERkZgZ+fH/fv+5OYmMh777XhwIE9BAT4M3jwMBo0sGfGjE9YtmwRnTp1486d2/JoSIlE8m8uaQLXrl3lyZPH8gjKFi1a4uFxhZ9+WoCZmTk3b/qQnp6Go2NLzp07g5eXJ4GBASQm5r/W0NCQH39cwqpVzuzdu5uUlGQGDfqIsLBQQkNDuXHDm4iIcJ48ecTt2zext29MWFgoc+d+hba2NkFBQTx79hQvL0+SkhLx87un6I9bEGjXrgMLF85j9ux5+PreYNSoIdy/74+9fWO6du3Bjz9+x9Gjh7C2tsHd3Y2goPskJibg6elBTEw0ISHB3Llzi5iYGG7d8iUkJJiYmBhF75bCKXWkZGni7JR9maWoX/GUIdtBXOdb/vGRRVH2fvE6IlJSEARBxZTqhNvPH40sr3oUKVuc/RfekXfdtpWR6G/vlkg1EwRUt22r+tf2ykxMOwiCICiASqaavam0tDS2b99CTk42c+Z8R3Z2NmPHDmffvsPFXo8rVA6KPuH3Nt421ex1/ezAgT0kJCRw+/ZNnJ1XY2Rk/FbvB5Camsr27ZvR0IDPP59T4Lno6Ch++GE+27ZV7FQ/lUw1e1NVqlTByqquPJtBR0cHZ+fV73zgjYgIJy7uGY6OLd/p+wrFq8xXO7yqn3l6ehAfH8+MGV9w/vw5oGwCpAwNDbG2tiEkJKjQcxYWNVi4cHGZvI8yq3TTDi8nhj1fAvmu5Obm4uz8y2sXeAiCMggODpL3GSenXhgZGZVZ2draRaf3qaurY2VlXWbvo6xU8rv2kSOHOHPmFL17f8Devbtp2rQ5v//+C8uWLaJ+/QZ4eFxmyZJfSU5OZu3aFTRs2IgLF87xySfTC5Tj5naaLVs2cPiwC3l5eYUSzHR0dNi0aR0WFjXIycnh4kV3vvhiNuHh4Zw540K3bk5MmZJf5oEDewC4evUfvv56PhJJHs7Oy+jVqw/Xr3sSGxvN+vX/49Gjh0RHR3Lhwjm0tLREipmgtG7evMHNm/lL3v/8czsnTx5j/PhJ7Nixlb/+OsTatSsK9Lf09HTWrl0hTxr087vLunVbqFmzFiEhD7ly5SKJiQloaGgya9bXAOTl5bFp0zrOnTvD7Nnz6NSpCwcO7MHd/Szbtu0iPT2NQ4f2o6WlxYUL7mzfvpuMjHT279+DmpoagYEB/PTTMvT09BT5Ub0RlTzybdWqDZGR4bRr15GdO/fi6+vDuXPnMDCowvDhozAxqYq3txdWVnXR09MnKyuL9eu30qpV6wLldO3andjY/JU2RSWYmZtXx8zMnOjoKGbM+IJp02byxx/bGDx4KOvWbeHgwX0AXLt2FYlEyogRY+jWzYn161dha1sfieS/JDMLCwt8fb1p1syR6tUt6NGjlxh4BaXWsmUrmjVrTrNmzRkxYgwxMdE4ODRl5cr13Lt3p1B/q1WrdoGkwdat23H58gXy8vIPRMaOncDnn88mMNBfvow+OjqayZOnMnXqZ5w6dQKAtm07kJiYCMD1616YmJgwZszHDBuWfzngzp3b6dChExMnTkFbW4ejRw8p5gN6Syp55KulpYWenj516lgC0LlzNx48eMCYMR/j4nKcZ8+eyhPCdHV1qVevfpF3mtDR0ZX/++UEs/r1G/y7jQ6WllZoaGhgZmaOoaHRv8loVcjMzG9Avr4+ALi6niQjI13+Xtra2vIks+JS1wRBFejq5veVevXqA2BrWw87u4aF+tuLSYP56YAZhIU9QSaToaWlBcCmTTvk5VpaWqKtrY25eXXS/51zf3Fq0N6+EVOnTiQoKJDJk6cB+f3N0tKKR49CqFGjpsqeLFfNWr/EyMgIAwMDtm3byNy5C7hz51apyyh9gtl/JBIJdetay28rVFxymkhzEioKf38/Tpw48tr+JpPJkEikREVFIpFI0NDQQCqVkpKSUmjbos6DWFjUYOfOvaxbt4rJk8fw119/I5FIaN7cUT4vXNKkQmWjktMOAFlZWfLBLDT0MX5+fvIj4YSEhAIJYSU5uVVcgllJODq2ZNeuHdy9e5uEhHhOnjz2yu2LS0UTBGX04kHD877k5uZabH972fPbbO3e/QdZWVkcOXKwxLfKunLlIrq6uvz001Lq1WtAdHQkjo4tWb78VyIjIwgNfcI//1x6m91TGJU98s3NzWH//j1oaGjQqlUbHBwaMmfOXGJiorG0tOTKlcvY2zvw8OED0tPT6dixC3p6ety+fZOQkIdER0fx5MkjAK5d86BVq9bs27ebjIwMrKzq4uPjxYABg/D390NDQ52nT2Px9vYiKiqSx48fERERBoCHxxW6du2Ov/9d5s79itq167Bo0TJ5+tqNG95Ur27Bw4cPyc7Oo1s3J1q3bsvmzev55pv5NG789tdMCkJ5iI6O4sYNb9TU1ORZva6uJ+jffxDt23fi559/LNDfWrVqx8OHD1BXV6d58xYEBd1HKpWSnp7ODz8s4bfflnLixFHmzl2ATCbj5s0bhIc/kfeTqKhIwsLy0/+SkhK5f9+fzMxM5sz5kvff70vjxg7Y2TVk0qRaLF36IxMmjKZt23YsXLhEwZ/Um1HJVLPo6Cg+/3wqf//93+1IlH2Zpahf8USqmWIpIs3sOWXvF68jUs0EQRBUjMoNvhKJhEuXLpCYmMCdO7cVXR1BEIQ3Uqpph3etNNkOyv71RdSveMow7VCZlXWGSmkoe794nbeZdhCRkoKA6rZtVR+8KjOVm3YQBEGoCJQ2UrIkX4Vu3rzBihW/snz5WszNGxa5zY0b3gQF3efCBXcmTvyETp26lkd1BRWnytMepYmUVOQUg1CQ0kZKliROsmXLVqSmFv+VSyKRsG7dSv78cz+dO3clLi6ujGspVBSV5VKzso5pFd6cyi6yeE5bW7vY5xIS4klPTwfAysq6UsTUCYKgGlRy8HVxOUZSUhIpKcmkpuavET9y5AhRUU/x9LzK5MmfYmVlzaFD+0lLS2P37p306vU+OTnZuLu78ezZU4yNTZg06VMOHNjLvXt3aNq0GSdPHmPkyLEMGTKMoKBAbt/2JTo6itzcXObM+Y6wsCcFXj9t2kwFfxKCUDZejm5cs2YT+/btxtDQEH9/P6ZMmYaVlTUpKSkcOrQPHR0dbt70ZfHiXzAwMGDv3l1oaWnj6fkPn332JXZ2DfDwuEJYWCi3bt2gf/+BNG7chE2b1mFsbMK9e3fo0aMXs2bNUPSuK4zKDb6PHz/i6tV/+OWXFeTl5XH06GEyMzMJCAhg6tRZWFnV5fffl/HXXwcZPHgoFy6cY9y4CQDMmTOLpUudUVdXZ+jQAXTt2h07uwa4up7g++9/+nfN+C8MGTIMF5fjDB06grp1rTlz5hQA69atKvT6Ro0cFPhpCELZeB7dOGjQUExNzdi4cQ29e/fB0bEl169f47vv5rBr1wHWrl3BiBGjsbNrSFRUJA8fPiAyMgItLS2GDx+FgYEB3t7XMDMz59y50yxa9Avt23dkxoxPOH78DGZmZkRGRrBq1QaysjIVvdsKpXKD7/nzbvK4R01NTYyNjbl16wZpaWnySEdLSyskEkmB12VkZBAeHoa7+1kA2rRpR1paGtra2piammFkZIyZmbk8HKRVqzbMmDGZkSPHMmLEmGJfLwgVwcvRjWvXruTjjycD+W09MjKCiIgwrl3zYO7cBQDy///9934GDBgMQL9+HwL5NxXIzMzE1fUkUqkER8cWpKamoKOji61tfQwNDTE0fLt7z6k6lRt8U1KS5bmgz0kkEszNzeWRjgMHfoS6unqhbbKzs/ngg36oq6vTt+8AcnJy8PO7W2C752tOOnXqgoXFepYv/4Vbt26yaNGyIl8vCBXBy9GNqakpJCYmYGFRAzU1NYyMjNHQ0EQikRIREYatbX6ub0JCvPyxtm3bv/CYBD09fXmf7NdvYKEDospO5a7zbdKkOZcuXSAjIx2JREJubi729o3Zt28f1655kJycxJEjB1FTU0Mmk8kj8AwNDTEyMmbDhtUkJiZw/fo1Hj16WOz7nD59Env7Rmzc+D9CQx+X+vWCoEpejm7s2LEzly9fBPLv+l2zZi1q165DixYt2bx5PampqXh7exEbG0OLFi3Zv38PkZERREZG4OvrQ+PGDnh6enD2rCspKckcOXKQvLw8QORaP6dyg2/Pnr1p374j06dPZufO/2FsbExg4H1mzpzJ0qU/MX36ZJo2bU5OTg5ubqeJj4/jn38ukZuby8KFi/H1vcGoUUO4f98fO7uG8pjIsLAneHl5kpSUSECAH3fu3GbZskW4uBxn4sRPAAq93t6+sUI/C0EoK8+jG48fP0Ljxg7Mm7eQ4OAgtmzZwJ49f7JgwU8AzJr1DampKQwb9iFBQfdp1MiBQYOG0qyZI5MmjWHLlg107doDMzNz5s1bwObN65kwYTQ1a9YmPT0Nf/97+Pr6EBMTo9gdVgJKGylZ2pg7ZV9mKepXPGXIdqhM1/kqUztU9n7xOiJSUhAEQcWU6oTbzx+NLK96FJL97w35BOFdeJdtW5FEv1IeItVMEFDdtq3qX9srMzHtIAiCoAAqnWpWFubMmcWkSZ8WWKkWFxfHpk1rsbS0YsKET96qfGfnZbRp046hQwcW+fzNmzdYufI3nJ3XULNmrbd6L+HNKfqE39soSarZm/SnnJwcdu/+g6Cg+/z+++oCz1244M6GDas5fNilVGUK/1HpVLOy8OWXc7CwqFHgMTMzM8zMzN/4onAXl2P07z8IgPHjJ2FkVPwdilu2bEVysriFvKJV9KsdStOf0tLS8PHxonv3njg4NOXWLd9C23Ts2Ikffpj/ynIkEglnz7rKF1oIBVX6aYfateugqVn4b9Cr0tJexcfHi3Pnzsp/trCogZ6e3itfo6Oj80bvJQhlTSaTsXq1M5mZ+bkLxfUDHR3d15a1Y8dWoqOjyrR+FYnKLS8+cuQQZ86confvD9i7dzdNmzanY8fObN++mTFjJrBjx1YOHDjK+fPnSE1NkaecNWvmiIvLMXJz83B1PcGsWXPQ1NRg1Spnpk79jJYtWxEeHsaxY39TvboFV65cokuXbgCF0syKS0MbMGAQV6/+Q3R0FHv37qZTp86sXbsSJ6fejB8/Cje3M4SEBJOQEI+9fSM++miEYj9ModJLT09jzZoV1K/fAA+Py0ycOIUnTx6Tm5tDtWqm8sH3r792cuLEUUaPHsegQUMLlfNyglnDho0JCPAD8vNYnJx6c+DAHiA/9+Hrr+dTt671O9tPZaRyR76tWrUhMjKcdu06snPnXnx9fZBIJERGRuLg0JSVK9cTGRnJgwdBjBgxhpEjR/P778sAcHd3Y+DAISxcuAQ1NTXs7RvLlx8D/PrrEsaM+ZgRI8bQpElT+ePr1q1i7NgJfP31fM6cOUVISDB2dg0IDw/9t7zFHD9+GC0tLbp06U716haMHj0OKytr9PUN5Mspjx49xKhR4xgz5mP+/vvAu/3gBKEId+/ewcCgCsOHj8LEpCpxcc+wsbGlbdsOtGvXAYD4+DgGDvyIBQsWcfz4kUJlJCYmcu7caUaPHseMGbP49defqVatGk2aNKNJk2Y4OfXm2rWrSCRSRowYQ7duTqxfv+pd76rSUbkjXy0tLfT09KlTxxKAzp278ehRCAD16uWHfRw+fID09MIpZ/r6BkyZ8jHTps2kdeu2wH9f+SMjI4iJiaZaNVMA+f9Lm4b2shenFDZu/B9eXp48fvyIXHG9paAE2rfviJ1dQ1xcjvPs2dMi26WpqRmGhoaYm5uTlpZe6PmAAL8iE8xe5OvrAyDvk6amZuWzQypE5QbflxkZGRWaU5VIJJiamhVKOVu0aBknTx5j4cJ5fPPNt/Ts+b78NampKUUOoKVNQ3uVxYsXMnLkWKytbThy5OCb7K4glCl/fz9OnDjC3LkLuHPn1iu3zQ+rkhZ6vCQJZhKJhLp1reXbiERAFZx2AMjKypIPdqGhj2nfvhOAfAqhefOWHDv2d6GUs7NnTzFkyDC++eZb/PzuFSizbl0bAP755xIA2dnZZGdnlzrNTENDg7y8PLKysgo0sMTERDw9/8HGxpa4uGdIpdJij5YF4V1xc3OVf4tMSEhAKpWgrq5Obm4uKSkluwqnuASz530hJSUZR8eW7Nq1g7t3b5OQEM/Jk8fKca9Ug0oOvrm5Oezfv4eDB/fRqlUbYmKiAXB1PQFAw4b2TJw4pUDKGcCGDWvZs+dPHj0KYejQEYSFhRIZGYGPz3W0tbX5/vtFrF+/mt9++5mIiHDi4p4RF/esVGloNja2JCYmsGPHFpKSEgkJCeb27Zvo6OjQuHETpk2bSFhYKDKZjJs3fQgMvE9iYgJXr15R2OcpVF7t23fiwIG9rFz5G5aWlly5chlHx5YcOLAHP797eHl5EhUVSWjof+08MDCAa9euAvkn2opKMNPV1cXRsSXnzp3By8uTrl2707Nnb+bO/Yo5c76UZ/9WZiqXahYdHcXnn0/l779PFnhc2ZdZivoVT6SalT9lSzN7Ttn7xeuIVDNBEAQVo1KDr0Qi4dKlCyQmJnDnzm1FV0cQBOGNlWra4V3Kzs0lLSW7xNubmlYhPl55vzaK+hVPXV2NqlUNFPLelUVp+9O7ouz94nVeV/9XtW2lHXwFQRAqMpWadhAEQagoxOArCIKgAGLwFQRBUAAx+AqCICiAGHwFQRAUQAy+giAICiAGX0EQBAUQg68gCIICiMFXEARBAcTgKwiCoAAlupPFnj170NLS4unTp4wfPx4jI6PyrlchaWlpfPvttwQHB2Ntbc3KlSvR19dn/vz5eHp6oqenx99//42hoaHC6hsXF8eIESPIzc2lV69efPnll2zduhVra2uys7MZPXo0oJjPMzw8nKFDh8rfSyqVcv78eVatWsXRo0dRV1dn165dWFlZcerUKRISEkhOTuajjz6iZs2a5V4/RVCGdv06RbX78PBwzp49i5GREQ0bNqR9+/bk5uayfv16rK2tefbsGZ9++qmiq15AZmYmI0eOZMOGDWhqarJ3715q1aqFkZERffv2BWDTpk1Ur16dmJgYpkyZ8sZ3EC8PV69eJSEhATs7O0xMTMqm/rLX8PPzk82ZM0cmk8lkPj4+sqVLl77uJeXiwoULspSUFJlUKpV99dVXst27d8v8/f1lp06dKrCdIuu7YcMGWXp6uvznJUuWyDw9PWUymUz2/fffywICAhRWPx8fH1lmZqZMJpPJYmNjZStWrJDFxsbK/vzzzwLbxcTEyCZOnCiTyWSy6Oho2YwZM95J/d41ZWnXr1NUux86dKi8nY0ZM0aWmZkp++OPP2QHDx6UyWT57fDcuXOKrHYh27dvlw0bNkwWHh4umzZtmiw0NFQmk8lk06ZNk8XGxsrc3Nxkq1atkslkMtmxY8dkO3fuVGBtC/r7779l+/fvl/9cVvV/7bSDu7s79evn35iyUaNGuLu7v/2fkTfQvXt3DA0NUVNTo3nz5hgbG/PPP/+wfPlyxo0bR2hoqELrm5WVhbe3N7169WL58uXk5eXh7u6OnZ0dAA0bNuT8+fMKq1+rVq3Q1dUFwNXVlb59++Lh4cHevXsZPHgw9+7l31bpn3/+wdIy/7YyNWrU4M6dO4Xux1URKEu7fp2X231aWhppaWno6+sDUK1aNe7evcu5c+do0KABkL8/58+fV2S1Czh27Bh9+vRBR0eHnJwcrl+/jpWVFQDW1tZcvXq1UP2V5ffx9OlTNmzYgIaGBt9++y137twps/q/dvCNi4vDxMQEyL8T79OnT99iV8pGREQEffr0YerUqVy4cIFBgwYxdepUQHH11dXVZefOnZw5c4aQkBD+97//ERcXh7GxcYG6KMPnGRQUhL29PUOGDOHMmTN8+eWXzJw5k7S0tAL1A1BXVyc+Pv6d17G8KcPvobQiIiJ477335G0KQFtbW96uXm5rysDHxwcrKyv51FVSUhIGBv9FLCp7/S9cuEDnzp0ZOnQoAwcOZMSIEfI/fPB29X/tnK+pqSmZmZkApKenY2pq+qb7USbOnTvHxIkT0dT8r+offfQRrq6uJCQkKLy+hoaGLF68mAULFmBqakpWVhZaWlqkp6dTrVo1AIXW7/Hjx9jY2BR4rGvXrnTt2pVHjx5hampKcHCw/Lns7OwCnb2iUHQ7Ka3n7f75zVmfe173523t+WPP25qi/fXXXyQkJABw//59lixZQlxcnPz59PR06tSpo7S/j5SUFKpUyb8NULt27TAyMipwMPI29X/tkW/v3r25f/8+AA8ePKBHjx5vtBNlwcPDgwYNGlCrVi1iY2PldyuWyWTUrVuXatWqKbS+sn+jkePj4+nevTu9evUiICCgQF0U/XmeOnWK/v37A//d7RlAX1+fBg0a0L17dx4+zL87c1RUFM2bN0dHR+ed1vFdUPTvoTRebPc6Ojpoa2vLB9q4uDhatGhB79695W0tKChIafZnzZo17N69m927d9OoUSPWrVtHt27diIiIAODJkyd07txZaX8fbdq0ITAwEMi/k06dOnXKrP4lClP/3//+h7GxMVFRUUyYMEEhR0I7d+7kzz//pGrVqkilUpo2bYqfnx+Ojo7Y2trSp08fzMzMFFbfq1evsnz5cnr37k3t2rX58MMPSUpKYvPmzdSvX5/s7GzGjBmjsPo9t2DBApYuXQrAJ598grm5Ofb29nTt2hVra2sADh8+TFZWFvHx8Xz00UfUrl37ndXvXVKGdv06L7f7Zs2aMWzYMC5cuICxsTF2dnZ07NiRnJwcVq1aRf369YmPj2fKlCmoqakpuvoFjBs3jl9++QWZTMa+ffuwsrLCwMCAAQMGIJPJWL16NVZWVkRFRTF16lSludph+/btVKlSBalUSvPmzTE0NCyT+os7WQiCICiAWGQhCIKgAGLwFQRBUAAx+AqCICiAGHwFQRAUQAy+giAIClCiYJ2K5u7du8ydO5fatWvz3nvvkZuby8mTJ1m3bh2NGjVSdPUE4Y2Jtq06KuXg26xZM5o3b069evXk6U8ffPBBgUUHgqCKRNtWHZVy8AUKXYDesGFDcnNzFVQbQSg7om2rhko7+L7o8ePHJCcnc+XKFQICAjAxMSEpKYn169ezbds2NDU1uXbtGr/99hvm5uZ4e3vj6+tLSkoKAQEBfPbZZyQlJfH5558TFBTE3bt3GT9+PC4uLtSpU4cjR46QkpLCpUuX+Pzzz6levTrLli3D0dGRBw8ecOvWLXbt2kWdOnUICgri3LlzJCQkoKGhwdSpU5kyZQpVq1Zl3bp1qKmpMX36dL7++muaNWum6I9OUHKibSuvSn3C7Xkk5ezZs9HR0aF58+Y8ePCAb7/9lrlz53Lw4EFsbGyYMmUK9vb2bNy4kbi4OHbu3Mn06dOZN28eqampyGQyevfuLS+3WbNm8mCT4OBgAgICmDBhAhMnTuSHH37A0tISXV1dkpKSWLFiBR07duTcuXPk5uby448/8umnnzJ//nzu3buHnp4e3333HWlpaRgYGKCnp4ejo2OlaJzCmxNtW/lV6iPfzp078+mnnzJu3DhSUlLQ1dWldu3aGBsbY2xszOrVq2nSpAlHjhxBV1cXTU1NLl68WCAV7MV4vKJcv36dtLQ0jhw5Qnp6OtbW1kgkEnR0dORZv9WrVyc9PZ3Hjx8jk8nka8L3798PQOvWrcnMzCQgIID09HRatWpVTp+IUFGItq38KvXg+5yFhQUWFhZcv369wON5eXk0atSIzp07A5CTk8OuXbtIT08vcdl5eXmYm5szZMgQAEaMGIG6euEvHDKZDKlUSnh4OBKJBA0NDaRSKUlJSVSrVo3Ro0eze/duatWqxYwZM95ib4XKRLRt5VVppx2kUilFZQq9eFa4TZs2/P777wQGBhIdHc3x48dxcHDg7NmzREVFkZaWxrNnz+TbGxgYEBsbS0REBOnp6WRnZ9O6dWv27dvH5cuXSUxMZO/evcWmTdna2qKmpsbmzZvJzMxkz5498rtPfPjhh1y5cgV1dXU0NDTK+NMQKhLRtlVDpRx8/fz8uHv3Lp6enoSEhACQm5vLpUuXePjwIXfv3gVg9OjRODg4MHr0aObPn4+TkxPt27enX79+DBw4kE2bNsljLCE/Mm/8+PFcvXqV2rVrc/XqVRwcHJg5cybz5s1j1KhRtGzZkpiYGAIDA7lx4wbh4eHy+qSlpbF8+XKOHDnCBx98QN26deWp+QYGBrz//vt079793X9ggsoQbVt1iEjJtzRu3DhmzpxJ27Zty/29Nm/ezLRp08r9fQQBRNsub2LOVwXcvXsXqVRa4N5qglARVOa2XSmnHcpKcHAwT548wdPTk5ycnHJ7n3nz5rFt2zb5iQ1BKG+ibZc/Me0gCIKgAOLIVxAEQQHE4CsIgqAAYvAVBEFQADH4CoIgKIAYfAVBEBRADL6CIAgK8H/9TJBwmfWzSQAAAABJRU5ErkJggg=="
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## End vis_new\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "source": [
    "\n",
    "words_ranks = {}\n",
    "words_ranks_correct = {}\n",
    "gtclass = 1\n",
    "\n",
    "for word in zip_data:\n",
    "    words_ranks[word]=[]\n",
    "    for rank, pred, truth, idx in zip_data[word]:\n",
    "        if truth==gtclass and pred==gtclass:\n",
    "            words_ranks[word].append(rank)\n",
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "source": [
    "# len(zip_data['Ġemployer'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "7600"
      ]
     },
     "metadata": {},
     "execution_count": 144
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "source": [
    "WordList2 = ['Ġsports', 'Ġboxing']\n",
    "WordList1 = ['Ġpolitics', 'Ġrepublic']\n",
    "WordList3 = ['Ġcompany', 'Ġtourism']\n",
    "WordList4 = ['Ġtechnology','Ġrobotics']\n",
    "\n",
    "# words_ranks = {}\n",
    "words_ranks_correct = {}\n",
    "# gtclass = 3\n",
    "\n",
    "for classidx, wordlist in enumerate([WordList1,WordList2,WordList3, WordList4]):\n",
    "    for word in wordlist:\n",
    "        words_ranks_correct[word]=[]\n",
    "        for rank, pred, truth, idx in zip_data[word]:\n",
    "            if truth==classidx and pred==classidx and rank<10:\n",
    "                words_ranks_correct[word].append(rank)\n",
    "\n",
    "from collections import Counter\n",
    "words_ranks_counter = {}\n",
    "for word in words_ranks_correct:\n",
    "    c = Counter(words_ranks_correct[word])\n",
    "    l = []\n",
    "    for i in range(10):\n",
    "        l.append(c[i])\n",
    "    words_ranks_counter[word] = l\n",
    "\n",
    "print(words_ranks_counter)\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'Ġpolitics': [9, 39, 42, 33, 38, 29, 33, 23, 15, 28], 'Ġrepublic': [33, 40, 36, 58, 53, 41, 42, 50, 53, 42], 'Ġsports': [18, 75, 73, 75, 84, 69, 71, 82, 80, 75], 'Ġboxing': [21, 2, 0, 2, 8, 9, 14, 17, 14, 16], 'Ġcompany': [32, 59, 41, 44, 38, 36, 22, 36, 23, 31], 'Ġtourism': [7, 3, 7, 11, 6, 6, 8, 5, 5, 5], 'Ġtechnology': [20, 30, 56, 70, 78, 59, 65, 67, 71, 50], 'Ġrobotics': [21, 16, 25, 32, 25, 26, 29, 24, 25, 33]}\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "source": [
    "\n",
    "fig, axiss = plt.subplots(2, 4)\n",
    "for wordid, word in enumerate(WordList2 + WordList1 + WordList3 + WordList4):\n",
    "    sns.histplot(y=np.array(words_ranks_correct[word]), kde=True, ax=axiss[wordid//4][wordid-wordid//4*4])\n",
    "    # sns.histplot(y=np.array(words_ranks_correct['Ġpolitics']), kde=True, ax=ax2)\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "source": [
    "[[len(words_ranks_correct[word]) for word in wordlist] for wordlist in [WordList1,WordList2,WordList3, WordList4]]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[[289, 154, 448], [702, 103, 643], [362, 39, 23], [566, 14, 256]]"
      ]
     },
     "metadata": {},
     "execution_count": 230
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "source": [
    "stds = {}\n",
    "means = {}\n",
    "for word in words_ranks:\n",
    "    a = np.array(words_ranks[word]).astype(np.float)\n",
    "    std = np.std(a)\n",
    "    stds[word] = std\n",
    "    means[word] = np.mean(a)\n",
    "sorted(means.items(), key=lambda x:x[1])\n",
    "\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/hushengding/miniconda3/envs/ptuning/lib/python3.7/site-packages/ipykernel_launcher.py:4: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[('Ġchampionship', 26.707602339181285),\n",
       " ('Ġathletes', 27.850611376927166),\n",
       " ('Ġteams', 33.20361509835194),\n",
       " ('Ġteam', 37.853801169590646),\n",
       " ('Ġcoach', 37.87134502923977),\n",
       " ('Ġsports', 38.611376927166404),\n",
       " ('Ġathlete', 43.33652312599681),\n",
       " ('Ġsoccer ball', 52.80861244019139),\n",
       " ('Ġclubs', 53.91600212652844),\n",
       " ('Ġseason', 54.82083997873472),\n",
       " ('Ġathletics', 55.727804359383306),\n",
       " ('Ġleagues', 55.780435938330676),\n",
       " ('Ġsports man', 64.53694843168527),\n",
       " ('Ġsports field', 67.35672514619883),\n",
       " ('Ġclub', 69.78415736310473),\n",
       " ('Ġathletic', 73.11483253588517),\n",
       " ('Ġhockey', 79.64221158958001),\n",
       " ('Ġsports person', 88.57097288676236),\n",
       " ('Ġbaseball', 93.67729930887826),\n",
       " ('Ġsports book', 94.00956937799043),\n",
       " ('Ġgame', 94.849548112706),\n",
       " ('Ġchampion', 95.68952684742159),\n",
       " ('Ġbasketball', 97.87240829346092),\n",
       " ('Ġtournament', 100.69856459330144),\n",
       " ('Ġgames', 104.22381711855397),\n",
       " ('Ġgame player', 105.01594896331738),\n",
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       " ('Ġproducts', 647.3785220627326),\n",
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       " ('Ġforeign', 650.3248272195641),\n",
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       " ('Ġfederal', 651.2892078681552),\n",
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       " ('Ġfederal ization', 668.5784157363105),\n",
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       " ('Ġelectricity', 669.1228070175439),\n",
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       " ('Ġtechn oid', 669.993620414673),\n",
       " ('Ġenergy', 672.53216374269),\n",
       " ('Ġbureau', 672.7639553429027),\n",
       " ('Ġsatellite', 673.4651780967571),\n",
       " ('Ġauction eer', 673.5491759702286),\n",
       " ('Ġunder arm', 673.7299308878256),\n",
       " ('Ġjew ish', 674.408293460925),\n",
       " ('Ġaust ral ia', 674.7575757575758),\n",
       " ('Ġstock', 676.0558213716108),\n",
       " ('Ġpolicy - making', 676.2525252525253),\n",
       " ('Ġdesign', 676.6990962254121),\n",
       " ('Ġmobile', 677.3668261562998),\n",
       " ('Ġmarketplace', 678.6794258373205),\n",
       " ('Ġshipping', 681.006379585327),\n",
       " ('Ġrocket ry', 681.5209994683679),\n",
       " ('Ġresources', 682.0659223817119),\n",
       " ('Ġneuroscience', 683.3200425305688),\n",
       " ('Ġexchange', 684.3684210526316),\n",
       " ('Ġpost industrial', 685.8314726209463),\n",
       " ('Ġtechnical', 686.2424242424242),\n",
       " ('Ġespionage', 686.6762360446571),\n",
       " ('Ġgovernmental', 687.6698564593302),\n",
       " ('Ġcomponent', 687.8639021796916),\n",
       " ('Ġmicro g raphics', 689.1531100478469),\n",
       " ('Ġministry', 689.2068048910154),\n",
       " ('Ġtechn ologists', 690.1515151515151),\n",
       " ('Ġproductivity', 691.2706007442849),\n",
       " ('Ġag ron omy', 692.8367889420521),\n",
       " ('Ġapplications', 694.1626794258373),\n",
       " ('Ġpetroleum', 695.6799574694312),\n",
       " ('Ġarchitecture', 696.6608187134503),\n",
       " ('Ġcopper', 696.8266879319511),\n",
       " ('Ġengineering', 697.3726741095162),\n",
       " ('Ġstandards', 697.6454013822434),\n",
       " ('Ġliberal ist', 700.4837852206273),\n",
       " ('Ġhardware', 704.8644338118022),\n",
       " ('Ġecological', 705.35778841042),\n",
       " ('Ġinnovative', 706.5821371610846),\n",
       " ('Ġoccupation', 708.123870281765),\n",
       " ('Ġmakers', 708.1632110579479),\n",
       " ('Ġrobotics', 708.7910685805423),\n",
       " ('Ġtrader', 710.2732589048378),\n",
       " ('Ġtechn ocratic', 710.3460925039873),\n",
       " ('Ġelection', 710.7490696438065),\n",
       " ('Ġmarket able', 711.2323232323232),\n",
       " ('Ġexport', 711.8426368952685),\n",
       " ('Ġcoalition', 711.9574694311536),\n",
       " ('Ġfocus', 715.3434343434343),\n",
       " ('Ġbi otechnology', 715.7740563530037),\n",
       " ('Ġge os cience', 716.7081339712919),\n",
       " ('Ġpolicy', 718.0116959064327),\n",
       " ('Ġnano', 720.665071770335),\n",
       " ('Ġprocessing', 721.2966507177033),\n",
       " ('Ġbusinesses', 721.8532695374801),\n",
       " ('Ġcapital', 722.0297713981925),\n",
       " ('Ġdemocrat', 724.1105794790005),\n",
       " ('Ġmaritime', 726.64221158958),\n",
       " ('Ġmultimedia', 727.6772993088782),\n",
       " ('Ġcyber market', 728.6081871345029),\n",
       " ('Ġdiplomat', 730.0010632642211),\n",
       " ('Ġparliament', 730.8474215842637),\n",
       " ('Ġconnectivity', 731.6028708133971),\n",
       " ('Ġgadgets', 731.882509303562),\n",
       " ('Ġdeveloped', 734.0),\n",
       " ('Ġtechn ocracy', 735.6874003189793),\n",
       " ('Ġrevenue', 737.4922913343966),\n",
       " ('Ġenvironment', 738.5135566188197),\n",
       " ('Ġenterprise', 739.4295587453482),\n",
       " ('Ġprivat ize', 739.7458798511431),\n",
       " ('Ġtechn os cience', 740.0930356193514),\n",
       " ('Ġengineer', 740.5725677830941),\n",
       " ('Ġmonopoly', 741.2004253056884),\n",
       " ('Ġelectronic', 741.3583200425305),\n",
       " ('Ġconsumer', 742.7751196172248),\n",
       " ('Ġretailer', 743.1270600744285),\n",
       " ('Ġcivil', 743.46783625731),\n",
       " ('Ġmining', 744.0031897926634),\n",
       " ('Ġglobal', 747.6331738437002),\n",
       " ('Ġcontainer ization', 748.8288144603935),\n",
       " ('Ġenterprises', 751.1956406166933),\n",
       " ('Ġmarket', 752.4635832004253),\n",
       " ('Ġparliamentary', 754.1143009037746),\n",
       " ('Ġcommerce', 754.4880382775119),\n",
       " ('Ġdata', 755.7432216905901),\n",
       " ('Ġfinance', 757.6905901116428),\n",
       " ('Ġmicro ph onics', 757.953216374269),\n",
       " ('Ġgovernment', 758.0574162679426),\n",
       " ('Ġenvironmental', 764.3157894736842),\n",
       " ('Ġdeveloper', 767.1685273790537),\n",
       " ('Ġgovernance', 767.1998936735779),\n",
       " ('Ġtechn ologic', 768.4152046783626),\n",
       " ('Ġdistribution', 768.4747474747475),\n",
       " ('Ġsupervision', 772.2706007442849),\n",
       " ('Ġentrepreneurship', 775.4609250398724),\n",
       " ('Ġgateway', 775.5821371610846),\n",
       " ('Ġmicro elect ronics', 775.6416799574695),\n",
       " ('Ġcyber commerce', 776.6645401382243),\n",
       " ('Ġcorporation', 779.6145667198299),\n",
       " ('Ġmarket eer', 780.0946305156831),\n",
       " ('Ġjurisdiction', 783.3269537480064),\n",
       " ('Ġresearch', 789.6390217969165),\n",
       " ('Ġmerchant', 792.0334928229665),\n",
       " ('Ġcommodity', 792.3285486443381),\n",
       " ('Ġtrust', 794.0754917597022),\n",
       " ('Ġfirms', 794.2817650186071),\n",
       " ('Ġindustry', 795.6565656565657),\n",
       " ('Ġtelecommunications', 797.9207868155237),\n",
       " ('Ġindustries', 798.5933014354067),\n",
       " ('Ġdemocracy', 799.5406698564593),\n",
       " ('Ġbusiness', 800.8399787347156),\n",
       " ('Ġvendor', 805.3328017012227),\n",
       " ('Ġcorporations', 806.5927698032962),\n",
       " ('Ġcore', 811.609250398724),\n",
       " ('Ġeconomy', 814.6608187134503),\n",
       " ('Ġcurrency', 816.9059011164275),\n",
       " ('Ġmicro', 818.6145667198299),\n",
       " ('Ġind igent', 828.0988835725677),\n",
       " ('Ġinnovation', 833.8335991493886),\n",
       " ('Ġsectors', 837.1701222753854),\n",
       " ('Ġeconomics', 837.4066985645933),\n",
       " ('Ġmarkets', 837.4742158426369),\n",
       " ('Ġmanufacturing', 837.8123338649655),\n",
       " ('Ġtechnological', 839.9883040935673),\n",
       " ('Ġsoftware', 840.8841041998937),\n",
       " ('Ġcompany', 844.8224348750665),\n",
       " ('Ġmultinational', 848.9787347155768),\n",
       " ('Ġnan otechnology', 853.4035087719299),\n",
       " ('Ġbroker', 857.5071770334928),\n",
       " ('Ġindustrial', 858.0978203083466),\n",
       " ('Ġcryptography', 860.5523657628921),\n",
       " ('Ġcompanies', 863.9510898458267),\n",
       " ('Ġelectronics', 866.0691121743753),\n",
       " ('Ġautomation', 868.3163211057948),\n",
       " ('Ġsector', 873.6044657097289),\n",
       " ('Ġbanking', 873.8048910154173),\n",
       " ('Ġtech', 876.8123338649655),\n",
       " ('Ġtechnologies', 878.5582137161084),\n",
       " ('Ġfinancial', 882.9611908559277),\n",
       " ('Ġinvestment', 891.8250930356194),\n",
       " ('Ġtechnology', 894.7001594896332),\n",
       " ('Ġtelecom', 900.611908559277),\n",
       " ('Ġstartups', 908.6331738437002),\n",
       " ('Ġeconomic', 908.977139819245)]"
      ]
     },
     "metadata": {},
     "execution_count": 231
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "source": [
    "zip_data[mykeys[0]]\n",
    "features[20]\n",
    "\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'text_a': 'IBM to hire even more new workers',\n",
       " 'text_b': 'By the end of the year, the computing giant plans to have its biggest headcount since 1991.',\n",
       " 'label': 3}"
      ]
     },
     "metadata": {},
     "execution_count": 214
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "source": [
    "# disp_w = [key for key in words_ranks][20:30]\n",
    "# disp_w = ['athletics','athletes', 'triathlon','cycling','skiing','race','olympics','championship', 'competitions','volleyball']\n",
    "# y = np.array([words_ranks[key] for key in x])\n",
    "# print(words_ranks_correct)\n",
    "disp_w = WordList1+WordList2+WordList3+WordList4\n",
    "data_for_violin = {'label words':[i[1:] for i in disp_w for j in words_ranks_correct[i]],\n",
    "                  \"rank\":[j for i in disp_w for j in words_ranks_correct[i]]}\n",
    "df_for_violin=pd.DataFrame(data_for_violin)\n",
    "fig=plt.figure()\n",
    "ax = sns.violinplot(x ='label words', y = 'rank', data=df_for_violin, alpha=0.9, bw=0.01, linewidth=0.05, width=1.3 )\n",
    "plt.xticks(rotation=45)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust()\n",
    "\n",
    "plt.savefig(\"../plots/violin.png\",bbox_inches=\"tight\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "source": [
    "from transformers import RobertaConfig, RobertaTokenizer,RobertaForMaskedLM\n",
    "model_classes = {'roberta': {\n",
    "        'config': RobertaConfig,\n",
    "        'tokenizer': RobertaTokenizer,\n",
    "        'model':RobertaForMaskedLM,\n",
    "    }}\n",
    "def get_tokenizer():\n",
    "    # args = get_args()\n",
    "    model_config = model_classes['roberta']\n",
    "    tokenizer = model_config['tokenizer'].from_pretrained(\n",
    "        \"../../plm_model_cache/roberta-large\")\n",
    "    # tokenizer.add_tokens(special)\n",
    "    return tokenizer\n",
    "tokenizer = get_tokenizer()\n",
    "\n",
    "orgdict= {}\n",
    "for l in L:\n",
    "    aaa = []\n",
    "    for x in l:\n",
    "        xp = \" \".join(tokenizer.tokenize(\" \"+x))\n",
    "        orgdict[xp] = x\n",
    "\n",
    "        \n",
    "Lp = [set(l) for l in L ]\n",
    "def find_in_vocab(x, mapping, vocab): \n",
    "    ret = -1\n",
    "    for i, v in enumerate(vocab):\n",
    "        if mapping[x] in v:\n",
    "            ret = i\n",
    "    if ret==-1:\n",
    "        print(\"Not found!\")\n",
    "        return -1\n",
    "    return ret\n",
    "    # x2 = \"\".join(x.split(\" \"))\n"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'L' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-216-2e1abf8409f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0morgdict\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0maaa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'L' is not defined"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# idx= 0\n",
    "num_instance = len(labels)\n",
    "word_with_class = []\n",
    "for idx in range(num_instance):\n",
    "    word_with_class.append([(orgdict[x], find_in_vocab(x, orgdict, Lp))for x in  topkchoices[idx]])\n",
    "\n",
    "# print(labels[idx], preds[idx], [(orgdict[x], find_in_vocab(x, orgdict, Lp))for x in  topkchoices[idx]])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Count the rank of each word in correctly predicted instance."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "\n",
    "\n",
    "words_ranks = {}\n",
    "cur_class = 1\n",
    "for idx in range(num_instance):\n",
    "    if labels[idx]==preds[idx] and labels[idx]==cur_class:\n",
    "        for rank, _ in enumerate(word_with_class[idx]):\n",
    "            word, wc = _\n",
    "            if wc==cur_class:\n",
    "                if word not in words_ranks:\n",
    "                    words_ranks[word] = [rank]\n",
    "                else:\n",
    "                    words_ranks[word].append(rank)\n",
    "\n",
    "           \n",
    "\n",
    "        "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "words_ranks = {key:np.array(words_ranks[key]) for key in words_ranks}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "# print(words_ranks)\n",
    "# tips = sns.load_dataset(\"tips\")\n",
    "# tips"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "source": [
    "# disp_w = [key for key in words_ranks][20:30]\n",
    "disp_w = ['athletics','athletes', 'triathlon','cycling','skiing','race','olympics','championship', 'competitions','volleyball']\n",
    "# y = np.array([words_ranks[key] for key in x])\n",
    "data_for_violin = {'label words':[i for i in disp_w for j in words_ranks[i]],\n",
    "                  \"rank\":[j for i in disp_w for j in words_ranks[i]]}\n",
    "df_for_violin=pd.DataFrame(data_for_violin)\n",
    "fig=plt.figure()\n",
    "ax = sns.violinplot(x ='label words', y = 'rank', data=df_for_violin, alpha=0.9, bw=0.15, linewidth=0.05, width=1.4 )\n",
    "plt.xticks(rotation=30)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust()\n",
    "\n",
    "plt.savefig(\"../plots/violin.png\",bbox_inches=\"tight\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "import seaborn as sns\n",
    "from brokenaxes import brokenaxes\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "# plt.style.use('ggplot')\n",
    "# sns.set(style=\"darkgrid\")\n",
    "plt.rc('font',family='Times New Roman')\n",
    "# sns.set_style(\"whitegrid\", {'grid.linestyle': '--'})\n",
    "dbpedia_mean = [72.9, 82.3, 82.4, 82.3, 82.5, 82.67]\n",
    "agnews_mean = [79.6, 82.1, 82.8, 82.9, 83.0, 83.1]\n",
    "imdb_mean = [89.3, 90.4, 91.1, 91.5, 91.5, 91.7]\n",
    "amazon_mean = [92.5, 92.2, 92.6, 92.6, 92.5, 92.7]\n",
    "\n",
    "x_axis = [0, 25, 50, 100, 200, 300]\n",
    "\n",
    "\n",
    "# bax=brokenaxes(xlims=((0,325)),ylims=((81,84),(90,95)),hspace=0.05)\n",
    "f, (ax1, ax2, ax3) = plt.subplots(ncols=1, nrows=3, sharex=True)\n",
    "# plt.figure()\n",
    "# plt.xlim(0,325)\n",
    "# brokenaxes(ylims=((81,84),(92,95)),hspace=0.05)\n",
    "# plt.xlim(0,325)498df9\n",
    "ax = sns.lineplot(np.array(x_axis), np.array(dbpedia_mean), marker='>', color='#91574d',label='DBPedia',ax = ax2)\n",
    "ax = sns.lineplot(np.array(x_axis), np.array(dbpedia_mean), marker='>', color='#91574d', label='DBPedia',ax = ax3)\n",
    "ax = sns.lineplot(np.array(x_axis), np.array(agnews_mean), marker='o', color='#f78923', label='AG’s News',ax=ax2)\n",
    "ax = sns.lineplot(np.array(x_axis), np.array(imdb_mean), marker='s', color='#a929ad',markerfacecolor='#a929ad',label='IMDB',ax=ax1)\n",
    "ax = sns.lineplot(np.array(x_axis), np.array(amazon_mean), marker='<', color='#6dbc5a', label='Amazon',ax=ax1)\n",
    "\n",
    "\n",
    "# for dataset in ['agnews', 'dbpedia', ]:\n",
    "ax2.set_ylim(79.4,83.4)\n",
    "ax1.set_ylim(89,93)\n",
    "ax3.set_ylim(70,74)\n",
    "# ax1.get_xaxis().set_visible(False)\n",
    "# ax2.get_xaxis().set_visible(False)\n",
    "ax2.set_ylabel(\"\")\n",
    "ax1.set_ylabel(\"\")\n",
    "ax3.set_ylabel(\"\")\n",
    "\n",
    "ax2.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax1.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax3.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax2.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "ax1.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "ax3.xaxis.set_major_locator(ticker.MultipleLocator(50))\n",
    "\n",
    "f.text(0.05, 0.50, 'Micro-F1', va='center', rotation='vertical', size=13)\n",
    "\n",
    "ax3.get_legend().remove()\n",
    "plt.xlabel(r\"$\\|\\tilde{C}\\|$\")\n",
    "\n",
    "d = .01  # how big to make the diagonal lines in axes coordinates\n",
    "# arguments to pass to plot, just so we don't keep repeating them\n",
    "kwargs = dict(transform=ax2.transAxes, color='k', clip_on=False)\n",
    "ax2.plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal\n",
    "ax2.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal\n",
    "\n",
    "kwargs = dict(transform=ax1.transAxes, color='k', clip_on=False)\n",
    "ax1.plot((-d, +d), (-d, +d), **kwargs)        # top-left diagonal\n",
    "ax1.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal\n",
    "\n",
    "\n",
    "kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes\n",
    "ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal\n",
    "ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal\n",
    "\n",
    "kwargs.update(transform=ax3.transAxes)  # switch to the bottom axes\n",
    "ax3.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal\n",
    "ax3.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig(\"../plots/calistudy.pdf\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/hushengding/miniconda3/envs/ptuning/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/hushengding/miniconda3/envs/ptuning/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/hushengding/miniconda3/envs/ptuning/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/hushengding/miniconda3/envs/ptuning/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/hushengding/miniconda3/envs/ptuning/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ],
      "image/png": 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wOLBYLFit1naPkWW5xSrbaCYQLSaTiczMTG9Poauo2gF4lmurgZ5ocdldNB5rouFYs3G3NPjc0TdiLW/5g9LoJMzpZmIzYxkwI53YAWZiM9zvYzPMmPqaujRO6tutlWUZjUbj3R7NRPv3K9qui8VioU+fPmRlZbUbd8vpdKLTRWb7u0pnWhRFoaKiAovFwpAhQ7pVhzo+qXYoKSmJmpR9/mixKtABDQ3+p+nJDpnGkmaD7hmLtzR6x+Wbyqz4poSVtBLm9BhiM2JJn9ofc4b77j22+a7e1C8GjTZ4D8LadGtVsrI72r9f0XZdrFZrh8Yf3L0EtTiAzrRIkkRKSgrl5eXdrkMdn5RKaW+a3oXvzOTHv/7kHYNvKmtqMdNG0kBMmvuuvf+5qW7jnmH2GvqYVFOPHrQJBL1FNObDDiU9/TxC6gDsdjurVq2iX79+FBYWctddd7Fr1y5WrFjB6tWrQ1k14I7cGM20t6jEUeegfGc55gwz/c7u5x6eaTbu5gwz5v4xaPSRaeCj/Zr4IrREHmq5+4fwaAlpDZ58AJdddhmPPPII3333HZMnT/aGhQ01ycn+5zlHMoqsUPrVcfJezSf7plF+jzEPMDPv80vC3LLgEI3XpD2ElshDq9X2dhOCRji0hDUfwA8//MDkyZO79MS6p/kAsrOzIzp+uSfOfHxMPPVfN3JgVS62Yjv6ZH270yedTgc5OTkRF7880HwAZrM56uLMt5cPICEhIerizLeXD6Bv374RnQ/A4XB422E0GnE6nbhcLgAMBgOKomC3293DIo0SriYXvg+/JElCY9RArPu9yWTC4XC0KcPzXdDpdGi1Wm+7NRoNBQUF3HTTTcyfP5/du3czePBgHn74YW8ZdrudGTNm8Nvf/pZzzjmHP/zhDwwfPpzs7GwOHjxIUlISd999N1VVVfz1r3+lvLycyy67jJ9//pm+ffvy61//GqPRiMvlwuFwIEkSer0eSZKw293TrbVaLXq93jsbytPe7uQDQAkh//73v5UHH3xQURRFWblypbJq1SpFURRlyZIlAZexf//+btffk3PDRUNpo/Lj339S3jvnQ+Xt7HeUz678j3L0gwLFaXMp9ZZ65e3sd9r81Vvqe7vZ3SYarkmgCC3hJZA2NjY2KoqihPS3s2TJEiU/P19Zs2aNMnXqVOXEiRPefW+++aYye/ZspaioSFEURbn33nuV7du3K4qiKLIsK7///e+VP//5z4qiKMo777yj/O1vf/Puu+iii5SDBw+20dIZ/j6XQK9nSHsA11xzDf/4xz9YvXo1n376KX/9619DWV0bjMbIjTNT+UsVeavzKfrEguJSyJg1gJHXDafvWSneBzstVwU60et13u3RSiRfk64itPQeRzcUcOTdgjbbPdNZx949xu951hNWdj7wvd99QxYNJmv+4IDbYDKZWLhwIWvWrOHOO+9ElmVqamrafZ4iSRLLly9n0aJF3HvvvS32NTY2Yrfb6dOnj3ebZ1puKAl7PoBwMnTo0LDW1xmyS+HY1mPkrc7nxPcV6Mw6hl8zlBFLhhM3MLbN8b7T9NRCpF2TniC0RB7hMJq+LF68mKuuuoqbb76ZL7/8khkzZvCf//yn3eMzMjK8aWrBPUy+Zs0aPvzwQ6ZNm9YiTW44nHJY8wEA5ObmUlhYSF5eHiNHjgxl9Rw8eJARI0aEtI5AcDQ4OPJuAQdfP0RDUQPmAWbG3XMGQ67IwtAnsOchkaKlp6hFBwgtvUnWfP9361arFZPJ1O4qZ1NfEzNWTwtaO/r378/kyZPZsGEDlZWVzJ49u8Pjjx07htls9j50HzNmDNdeey1XX3011113Ha+++irXX399Cy2hJKz5AABGjRrF9u3bQ1mtF89Dud6iobiBg68f4sg7R3HUO0kZn8zYu08nY1Z6l+fh97aWYKEWHSC0RCJK6DLctsv111/PzTffzF/+8pdOj125ciXLli1rM39fp9MxatQo78N1CI+W6B1MjlAURaFibyV5q/Mp3lIMkkTmRRmMvG44Ke2E3xUIBMHF9/lZ6+09IT8/H4vFwscff0xubi6DBg1i4sSJzJw5k2nTppGXl8exY8fYunUr06dPJy8vD41Gg8Vi4dChQ6Snp3PLLbdQU1PD999/z9GjR3nzzTeprKzk+PHj3pGScBHSpPDBoCdL1F0uV0jm0vpL3Kwo4LK62PXH76n8qQp9vJ6hVw1hxOKhmNN7HjAsVFrCjVp0gNASbgKxBYqiqGa1cKBa/H0uEZEUvrcpLS0NSRTN9kI0XLBqKvZaB+P/ZxxZ8wejjw3exxsqLeFGLTpAaIlEHA4HBoOht5sRFMKhJTLjBQQJz5P2cGFMMXLJxgsZsXhYUI0/hF9LqFCLDhBaIhHPgiw1EA4tqnYAoUJ2+h8105m0p1Q6OYFAEN2o2gFkZmYGtTxFUTi45hBNZeFN3AzB19JbqEUHCC2RiFqGfyA8WlTtADwxMoJB4/Emvrjpa/b85Ue0hvA/LAumlt5ELTpAaIlEInxOS5cQ00B7yPHjx4MS5bDwYws/PLIHl0NmwkNnEjvQHJIpZh0RLC29jVp0gNASiZxKCWGCQdjzAbzyyiskJydTX1/PkiVLQll9j7HX2Pnhsb0UbrSQfEYSk5+aSJ+sPp2fKBAIBFFASIeAPPkAFi5cSHV1NS+88ALV1dUsWLCAmpoafvzxx1BWT0pKSrfPPb6jjE8XbKHo02LG3JHNzDXTe9X490RLJKEWHSC0RCId3THbXTYa7HVBqeexxx4L+RBN1CeEaZ0P4JFHHuGpp54C3HHXt2/fzrhx4zosoyf5AGRZJj4+vkvxyzUuDY0fWDn4+iGMGQaG/mUwIy4aRtmJsrDFmQd3ICjfOPOyLJOSkhJ1ceZbXydZlqmqqoqoOPPdvU6yLFNfX6+KfACyLGOz2aI+H4DT6fQmU/fE8pclGbtiZbvlE6ZlXoSmyW32upMPwGg0kp+fz8aNGzn33HOZOXNmt8rwjdsfExPjbmfztffkA/BoCWU+gJCuBF61ahWHDx/m0Ucf5ZVXXuGpp57ixRdfZMaMGWzbto3PP/+cRx99tMMyerISuKvnVv5SxXf37qbucB0jlgzjjLtPR2eKjNWR0ZC0OxDUogOElnDj28adJV/y7bG2McVk2YVGo0UraclOGcfI5NP49th2dhzbilN28LuJD/Nh/tt+yz9nwHTOTp/aaTtWr15NWloaH374IStWrCA3N5eHH36YBQsWsGHDBv7rv/6LgoICvvnmG5555hkUReGf//wnI0eORKvVcu211/LAAw8wceJEtm/fTkJCAg8++CCvvPIK/fr1Y+/evdx///289tprWCwWzGYzu3fv5rXXXiMmJqbDz6Wjbf4I6RDQNddcg9ls9uYDmDdvntdDNTQ0kJiYGMrqA0Z2yvzyrxz+c802nA0Opr08hfEPjIsY4y8QCLrG9IEXMSxxFM99/xhfWD7DKQdnlpPdbker1TJ79mwOHDhAWVkZo0aNwmazsXDhQh555BG2bNnCXXfdxeWXX86uXbuoqKhg9uzZzJ07l88++wyA3//+90ybNo3CwkLuuece3n33XVJTU1m0aBEZGRm88cYbjBw50htSPyMjo0XvLliENR/AeeedxxdffMHcuXPJz89n6tTOvW1P8OctW1N3tJ7v7ttF5b4qBl2ayYT/ORNDQuTNJQ5ESzSgFh0gtPQmZ6dP9Xu3brPZvHH07S4b95/zFNuLPuELy2acsoN4QyJ3nvU/3a53y5YtOBwONmzYwOjRo1m/fj233norsbGxGAwGYmJiiIuLA04OMY0cOZLdu3djNBq9wzzJycncdttt/Pd//zdxcXHs37+fKVOmAO7hv08++YRRo0YRHx8PuK9PKKbqhj0fwHfffcf69euJj49n0qRJoayerKysdvcpisKht4/w499+QqPXcM7Tkxg0d2BI29MTOtISTahFBwgtkYhvEhWD1ohBa2Tu0CuZPvBithd90uNAcSUlJdx4440ATJkyhcWLF3PzzTd3eM66deuQZZmzzz6bF154AXBPkElOTub888+nsrKSESNG8PPPPzNnzhwaGxs57bTTupQ7vbuEdAgoNTWV888/n8suu8y77dZbb+XKK6/khhtuCGXVgDv5jD+aypv4cvkOfnh0L33Hp3DR+7Mi2vhD+1qiDbXoAKElEvE8GPXFoDWSZEph7tArMWq6n2Xr7bffpqCgwPtAV5ZlGhsbueeeezhy5AhHjx5l7969FBYWUlpayi+//ML+/fsZOnQob731Fm+88QYNDQ3s3LmTp556itGjR/P++++zevVqrrzySo4fP86aNWs4evQoV199Nbt37+bgwYMcO3YMi8XCzz//3O22t4eqw0H7O9fyWTG7H96Dq8nJ2N+fwfBrhkZF/J5oeEgXCGrRAUJLuAmkjU1NTVE3nNUegWoR4aADwF7nYM9ffqTgg0KSTk9k8pOTiB8qFnUJBIJTF1U7gNGjRwNQ9l05O+/fTVO5ldNuGc1py0ej0UdXGCSPlmhHLTpAaIlEQp1DN5yEQ4vqHIBvti6nw4ncKIMEZz08HkOCgZRx0RnvxGKxMHBgZD+nCAS16AChpTfoLEvWqZYQpqcj+KpzAO1l65r76UXEDYzthRYFB8+K0GhHLTpAaAk3JpOJiooKUlJS2nUCp1JCGEVRqKio6FFPQXUOoD2k6BrxEQgErcjMzMRisVBeXt7uMQ6HIyzTJ8NBIFpMJlOPcjmcMg4g2hk0aFBvNyEoqEUHCC3hRq/XM2TIkA6PaWhoIDY2env6voRDi7gvjhL8zW+ORtSiA4SWSEQtOiA8WkLqAJqamnjmmWfYvHkzTz/9NPX19fz73//mww8/5PHHH+/SeJ2axva6gycKZLSjFh0gtEQiatEBwdPSke0MqQP46quvSEpK4sILLyQ9PZ0vv/wSm83GZZddRmpqKhs2bPB7Xn19PV9//TX//Oc/OXr0KGeddRZms5k//vGPndapjdFx6eaLuHTzRczZNNP7OpTZugQCgSASOHHiBFu2bKG0tJTFixeTnZ1NTEwM69at83t8SFcCHz9+nF//+tesWLGCTz/9FLvdTkxMDDfeeCPbt29n8+bNPPbYY97jd+7cyXXXXUdeXp53etN//vMf6urqMJvN9O3bV1XzfAUCgSAYVFRUUFxc7M0ZkJ6ezq9+9SvGjx/PhAkTuOuuu/xGXw7pbXH//v257rrrePDBB5k/fz4JCQm8+eab3HjjjVitVsxmc4vjjUYjZ511FosXL2b8+PGMHz+ejIyMHgdwEggEAjXzwQcf8NFHHzFu3DjGjx/PmWee6U3I1BEh7QEUFRWxYcMGbrrpJm688UbuuOMOdu7ciU6nY+/evVx//fWcc845oapeIBAIBB0Q0h5ATk4OCQkJGAwGli1bxv79+7n99tspLS2lrKxMGH+BQCDoRULaA7DZbDz77LNMnjyZ3NxcrrrqKnJzc7FYLCxYsCAsSY8FAoFA4J+IDwctEAgEgtAgFoIJBALBKYpwAAKBQHCKIhyAQCAQnKIIByAQCASnKMIBCAQCwSlKVDgAq9XKnj17qK2t7e2mCAQCQdRQW1vL999/7w0R0ZqIm4hfU1PD3r172bNnj/cvJycHp9PJXXfdxTPPPNPbTRQIBIKIo7S0tIXd3LNnD4cOHQLg1VdfZenSpW3Oiah1ABs3buSyyy7zBoJLS0vj7bffxm63YzabiY+PR6vVBlye0+lUzWIztWhRiw4QWiIRteiArmkpKSmhuLjY+z4zM5N//vOf3mBwc+bM8Ws7I8oBFBcXs27dOkaNGsX48eNJS0sjJyeH7Ozs3m6aQCAQRCw//vgj27dvZ9y4cZx55pkcO3YsILsZUQ7AHz1xAIcPH2bo0KFBblHvoBYtatEBQkskogYdiiJDQwUOWyN6oxliU5C6mNQ8ULupjr5SO9hstt5uQtBQixa16AChJRKJdh2KIqOU5+F492aU2mLs8RnoF70E/UZ22QkEQkgdQFNTEy+99BJjxozhxx9/ZPny5Tz99NP8+OOPXH755dxwww2hrF4gEHSC525zaL8YlPrybt1tRgKKIoPsRHJaUWx1ILua/5yguFBk58n3Pts9rxV/+1zOnp3vs817vuJbTqt9shPd+b/D+ekfUWrd4/lKbTGOd2/GsGQ9xPUL+ucWUgfgmxKyrKyM9evXc9999yFJEr/+9a9D7gCGDRsW0vLDSbRr8RiaUQPio9rQ+BJJ10RRFGg2gm5D08ogya2NmBNFo0dy2XC8f9vJu835K3A5m5CsNS3Ob2HgvEasVdlt6mx9vq9x9NO2VkbTtz5Fbr3P0bINuEeyswDbx71wASQNaLSg0YHU/F9z8r+k0YLUcpv3v6RF0hlBY0YyxXuNvwelthhk/9M4e0pIHcDYsWN57rnnuOCCC2hoaGDp0qWYTCZKSkq4+uqrQ1k1AJWVlaSlpYW8nnAQzVpad2ulEHVr3XeB7RsaxY9h6vwu7qSxa30X11BThSbW7GPEWt/h+Rqx5vNdTj/HNNfvxzB2fBfbqr4uol/wAo7P/9LybnPD7ehm/hHH+7d2/QJIWv8GTqNDamMYW+2XtKA1tN3XbDQ1foxmm/I1WuoamuiTkNjmfKl1vW3Ob7/sk+fr2jlfG7TvsVJfjhSf0cIJSPEZoDEEpfzWhDUlZExMDBUVFTzxxBOUlZVx8cUXdzrNyel0kpOTA0Bqaiomk8mb6iwuLo7MzEwOHDgAgEajYdSoURw9epSmpiasViuJiYnU1tZSUVHhbZNer8disQAQHx9PWloaeXl5AOh0OkaMGMHhw4e944nDhg2jsrKSqqoqwJ1vU6PReKddJSQk0K9fP/Lz8wHQ6/UMHz6c/Px8HA4HAMOHD6e8vJyamhoAMjIykGWZkpISAJKSkkhOTvbO2zUajQwdOpSiwgKSYyQStS7k+nJONMpUVFQC7qleDoeD48ePA5CSkkJ8fDxHjhwBICYmhqysLHJzc5FlGYDRo0djsVior68HYNCgQVitVsrKygDo27cvcXFxHD16FACzycigjP7k7f8JSXYguewMy8qkpOgotsY6JNlOWt8krPU11FdXIMkO+piN6CWZmsoyJJed5ElXomy8u023VnPxk9RtexYUF2ajAafDhtNuA8WFQatBwoXDZgVFRispaCVwOqxIsgyKC62kILscSIoLZBkJOZCvZdCIAVqbXUXSIWl1yGhQJA1IWjQ6PYqkxaXQ/N6AVqfH5nC5jYfWgDHGTKPV0XyOkbj4VJqsdhyyApIWc1w8LgWarHbQaDHFxKE3mqipawBJg85oIjEphbITlSBpUSQN/dMzqKyuwe5woUha+qb2x2p3UFvfCJKWjKQhfu82XYlDKJ72LJJWR9bQ4RQfK8Vqd6BIWgYNHkJdQyOV1TUokpbU/unojSYsxSUgSb3+ezp65Agmk6nd39PBgwdxOt1XbeTIkZSWlnoXmLb8PSmkpCQQHxfk35PZzODBg702DSA7O5uCggIaGxsxmYwMWvgizveWe2+WtAv/hVVj5mjzOZ3Zva4Q9pSQZ599NgC/+c1vuOOOOxg7dmyHZfRkFlC0TyH1e+e88AUUQyySrR6cNhSXDZwn/1q8d9lRvK+b9zutzfvt4LL52e8+z1uG4uqZCI0Ow9WvYX/zmja7DNeuw7HlEfddlFbvvgv0d4fY5i6uvbvAAO7i/Nah69b5uQcPMSr7NJ+72Oga0lLqy7G/fmWbu03DkvVIIRhvDgfR/puHk8OldmsDBlNs9M4C8pcS0uMARowYQWZmZiirJz09PaTlhxqlushr/KH5zvm9W9HN/CP2gLvoEuhNoDWCzoikM3hfozMiaY1g7ON9r/HZ592vM0LzeZJnn7e85td6o5/9BiSNrt1urZSQiXHZ+8H/4MJE2qBhSPqY3m5G94lNQb/opbZDc7Epvd2ybhPtv/mmulp0BiP6uH40OfUY4xJDWl9IHcD06dN59tln2b59O4cPHyYzM5O7776bqVOnMnXqVJKTk0NZPRpNdN2RASiyE/ngZpy7/41+2u/9dtGl5KHoF7540gC3Nsg+7913plIvqWlGhYYGovP75YskaaDfSAxL1iM7bWh0xqh/OB/t18Rps7F7w3rGX3I5Wr0+5PWF1AEYjUbuuecewO0MAObOnRvKKltQXFxMfHx82OrrCYqtDte+tbh+eA2lxoKUMBAMcf7vnE0JaEbM7sXWdg1fQ9OTbm2kEU3fr/aQJA3E9SMvJ4fs7ND2yMNBT66JIsu4nA5cjuY/p/Pk/xbbff57jvFuP3ms3O55Tr/Hu5wOLrz1t+z56H32fbqRMTPnMPHyKzDG9UFvNAb5k3LTbQfQ2NiI2WwOZltOSeTqQlzfv4rrp3Vgb0DKnIR+xgNohs8CSVLNnbPH0BwuOkF2dlZvN0eAz3BDiIxLeyiK4ja2vkaxtYH0bm9pINs3yO5tleXlFH0e19ZQtzjP2Wa/7HQiu3r4vMsHSdKg1evQ6vVodfqW//V6NFr3PmOcscV+ncF9LVwOB/s+3ciBL7ZyxZ8eJ6F/OoaY4A83tusAjh071uGJn332Gddff32w2xNUEhISersJflEUBcWyG+fufyPnbwFJg2b0XHQTf4Um7XTvcU11tegSsjAsWY/T3oTOEBP1d86Rek26QzRpkV0unA47LrsDp92Gy+EAjcSu99cxbs6lmJE5ume3+5hWhrf9O9lWd8kdGfJWhpcgzj3xGFOtTgcaDXVGox/Da8BgjvV5r/PZr2thnH23a7zbTx6ja/6vae88vQ6NJvCglb7Ulrln9Gn1esbOuZTxcy/vnR7A3XffTX5+frvdqaqqqoh3AP36RdZMBsVlRz7wMc7d/0Y5/jOYEtFOvgnd+CVIfdrO8fcdD9TH9EVSQY8r0q5JT+iKFll2NRtAO06728h6jK3TbnMbZofPdrsdp8OBy3tM63M85bTd7nL4nNtcriK3nSK76KG/sHfjBn76bBNjZlzI6GkzOPDlNn75/DO3g/CDr6FraQBPvtabYjDF9Wlz19vC8Op0fvdr2mzXtTTknv0+7yWfcX+Hw4E+DGPnoULSaBg/byHjL7kMfYwZY4h/8+06gLvuuouMjAwGDRrkd//27dtD1qhgkZ+fH5IpYV3tOitN1bh+fBPnD69D/XGk5KHoLnwUKfty7A6ZhoZ6bGUHsTU2YGtowNbYgL2hgYFnjPMZD7yIM2ZfRIWlkIqiQrRaLVqDAZ3e4L4rMRjQ6pvfG/Sttuvb7NN0Iax2T/H9vEJ1TTpDUZTmO9Rm4+hjYJ0+htJ7jL2toW59TlVFBWaTycdoO3A6/BlzB7Kr6wu1WiBJ6Hyut1ZvQGfwXFf3f6M51u92z3fAe67BvS+mj7sH43I42PfZJg58uY1FD/2FifOvwuW0NxtYvXcoQ6ONgAkFndBb369goTMaOeeqxeiNxrBMaW3XAZx77rnexQ7+8DzUPRVp8aTeYMDe2NhsvOtbGHGpuoDkyi/p2/QzWpyUu1LJqz+LgqNmbF9twGF9q8N6Fj30F8AzHvgRB774nAV/fBSnzcaWF5/rkQZJo/FxCMaWTqN5e0dGxPveYGxxTssyTr7e/f46zrzkcuJMRhpra9zGtNXdqtPuaHX36mtwHf7PaceYtyzX3u4dbVdobVxdioIrLq5ZswFTXBxaQ/LJz0yvR2swNv83tNpuaPX5+vm8fc7RaLVBN76+ww1nzJnLhLnzQzrcIOicmD7hnVTQ4UPgaJ9SFaquoCLLJ+/M23Sd7WT0aeCM1EoGJdTjkiUKGvtzxDUSmykdY3IsgwbGYjDHYYyNxWiOdf+PjTv5uvm/ta4OaPsDTRs+ktFTZ5wcCnD4DAXY7a3uSO0tDGWbYYRmQ9tmu82Gtb6+hWH2HOO027s0hrvoob+wZ+MG9jUPNegDGGrwh0arczsZ753wSQOpNegxxJiJiU9o5Yx8DW6rc5uNblsHZmhjtLV6fRsDnJ+fz/DhwwNuf6ThO9xQb7URl9K3t5vUY6J5+Kc14dDS7krgK664giVLlnD55Zd3KQtXsInElX21Zcf55Lm/cf4V84ntE0djQyPG5HT0BZ+j+eVttDVHICYZ7fhr3eP73Zy1U3einL2ffMT4Sy6LqDszRVHcDxXtrR2P/7v5lMyBrPnDHd7zDTFmFj34GFq9gaoSS7u9Bt+hrZ48WBP4p7dmAQlCT49XAp9xxhksXLgQgA0bNvDBBx9w3XXXRdXQT6ju0LQ6LZfecBW6zfeg1BZjis9Ad8mTOI98AgYD2kueRJt9mXtRVg/wHQ+MpLtNSZKaZ07ogM4fUnU01JAy0P8zpmggkq5Jd/Adboh2LR7UogPCo6VdBzBgwADv6/nz57Nnz54Wxl9RlE7HJFvnA7j55pt58skn2b17NyNHjuR///d/MRhCE+UO8AaOCjZGjQNXs/EH9+pc58f3of+vfyMlDQnaWK3vDzRUWsKB71BDTWOjKoYaILqvSWvUokUtOiA8Wtod5G9txFpPB/3Pf/7TaeG++QDS09N5/fXX+d3vfsemTZuorq4OqIxIRMLlP0SDzhjxsyR6A09PJi6lL7X1Db3dHIFA0Ey7PYBXX32VTZs2ed+XlZXx5ZdfAu67f4vFwu7duzssvHU+gOuvv56Y5tVsp512WsjnhIeq+yTpjGGN2Q2h0xIOfHsy0ayjNUJL5KEWHRAeLe06gHHjxjFz5ky/d7SyLLN169ZOC/eXDwDA5XLR1NTExIkTOy2jJ/kAHA4HI0eODHo+gEFyAaZLnsD58f3eEA26RS9ytLwOa9GJoOYD8MQvdzgcjBkzpoP45SHKB9BJ/HKArKws6uvrOXHiRKfXyeFwYDQa28QvHzJkSNTlbXA4HMTFxXUxznxkXieHw0FSUlKnceYj/ToVFhai1+uDkA+g969TSUkJer2+y3avSyjtcPjw4fZ2KYqiKEeOHOlwv6IoSmFhofL8888rNptNWbJkifLdd98piqIoGzZsUEpLSzs9X1EUZf/+/QEdF+xz20NuqFCanpuolK++QSn7Zaci11gUua5MkWVX0OvyJRRaegO16FAUoSUSUYsORQmP7Wv3GcCQIUP8bv/b3/7m9VKd4S8fwLfffsuYMWPo37+/904kmnBsexJsdWzfXceebV+5o3PG9Yvq+DwCgeDUpMvRQF9//XX+8Ic/BHRs63wA/fr14/777ycpKQlZlpk9eza33357lxsdKBkZGUEtz3X0a+Sf34UJN1K8+xumzA7fFMZga+kt1KIDhJae4nA4sFgsWK3WoJUpy3KL4ZVoJhAtJpOJzMzMbi8a67IDULqwAtRfPgDP2oJw0FEoi66iOJpwfvY/SElZVKRfAnxDysDBQSu/M4KppTdRiw4QWnqKxWKhT58+ZGVlBW32nNPp7DTPeLTQmRZFUaioqMBisbQ7YtMZXR63mDJlSrcq6g08D+6CgXPH8yjVRejm/JmKklIAkjPD1wMIppbeRC06QGjpKVarlZSUlKBOnT6V1gFIkkRKSkqPelABOwBZlsnLywt4+EdNyGU5uHauRHvGlWgHn0tFUQF6Uwx9+qontLFA0BuIdTM9o6efX0B9pYKCAm666SYKCgowmUwMGzaM5557LuLHQJOSknpchiK7cHzyR4hJRHfBfQBUWgpJzhgY1i9vMLREAmrRAUJLJKKW4R8Ij5aAegD/+7//y+23386ePXvYu3cvK1as4M033wx123pMMJLOu354DaV0H/pZDyLFJAJQYSkkZeDAHpfdFYKhJRJQiw4QWiKR3gxc6Y/6+nr++7//m507d6IoSpeSaIVDS0AO4KyzzuKyyy7zLuRKT0/3vo5kPItAuotSU4zzy7+jGTodzehLAWiqraWppobkMD4Ahp5riRTUogOElnDSVFeLo3khWUfYAjjGw+7duxk3bhzPP/88r7zyCnfeeSfr16+nsLCQBQsW8Mgjj/D2229z9913s23bNgA2b97M+PHjefHFF1m+fDlvvdVxTo+4uDjS09O9sdNWrlwZcPu6oqW7BNTHqK6ubvFE+uuvv+bbb7/ltttuC2njehNFUXBsfhhQ0F34iHe4p8LiXo2XEsYHwALBqY5vEqZghUafOHEiKSkp3HTTTRiNRhobG1m0aBEJCQmMHj2aGTNmMG3aNC644AIWLFjAN998w4UXXojZbGb58uWUlJQwa9Ys5s6d227qXHDPhvQQaT2UgBzA7NmzvXkBysvLkSSJ559/PtRt6zHGHnxJ5NxNyIe3opvxAJqETO/2iqICILwzgKBnWiIJtegAoSWY5HzxOfu3bml3/3mLr/NJjzqHMTMupOjnfRTs/aFFuk1ZkdE0L8o8bcZssqfNDLgNZrOZ6667jjfeeIP+/ft7t5eVlfmNW2a1WomLiyMmJoadO3dy/Phxtm7dypIlSxg3bhwvv/wyGRkZbNu2jbPPPptvvvmGNWvWsGLFCl588UUSEhLYt28f999/v18HEo6EXAE5AJPJxNq1a/n++++RZZlJkyYRFxcX6rb1mKFDh3brPMVag+M/f0bqfzras65rsa/SUoghxkxccveSvHSX7mqJNNSiA4SW3sCdHnUjB77YysL/eZTE9AHs+eh9735ND1fkZ2RkcPz4cfr378+2bdvIzc3lrbfe4o47TiY0stvtfPbZZ+Tk5LBy5Uq0Wi2vvfYaV111FRMnTiQvL49jx46RkJDAvHnz2L9/PwCTJk3ihRdeANxz/K+55hpyc3PJy8vzGxctHE45IAewZMkS3njjjS4ng2mdD2D58uVYLBYeeOAB3n333W41uCscPHiQESNGdPk857anoLEK/ZUrkTQtPyL3A+BBYZ++1l0tkYZadIDQEkyyp83s8G7dN6nQ2IvmebPk9R82kqFnne09zmq1YjKZut2OkpISBjZP8LjggguYNm0aixYt4uKLL2b48OGcfvrpGAwG5syZw5w5cwA4ceIEtbW1TJs2DXBPmX/sscc477zzALzPS31n9Zx77rneiTQul8tvW3qqJRACcpcPPPBAmwcSb7zxRqfntc4H8M033zB69OiQJoHxxRP1ryvIhd/h2rcW7aRfoek/psU+RVHcU0B7Yfy/O1oiEbXoAKElnHiSCi175iVvbgl/zwG6EqmgNTabjTVr1rBs2bIW21NSUkhNTfVGL21NUlISBw4cYN++fTidTr788ktSU1P54YcfvMe0Xml97733cs0115CYmNhum3uiJVAC6gH8/e9/p6SkxHvX63mivXjx4g7Pa50P4Pzzzwe6luy4J+GgrVYrVqs14PC1kstOxhf3YEgYiGXAZVib6/WEry0tKsRaV4c5pR+1tbUhCzMMbcPXWq1WXC5XRIevDSQctNVqJTc3N+rCDPu7TlarlcOHD0ddmGF/18lqtVJUVBTWcNAOh4Ompib0ej2SJGG32wH3g1KdTuc9XpIkdEYjExdcBVotTllGK8s4nU7v3bPBYEBRFBRFoampCZ1Oh1ar9Zah0WgwGo1YrVavYc3NzaWyspKVK1diNBrJzc3l5ptvJjU1ldzcXBRF4dixYxw4cICJEycyceJEtm3bRnV1NV988QWTJk3CZDLhcrm4//77Wb58OWPGjOGJJ55g9OjR/O53v+ORRx6hqqoKnU6HXq+npKSEsrIyMjIy+O1vf0tcXBxbt25l/Pjx3mtvNBpxuVxeLf4+H71e710B7PnuBjUctC9r165VmpqaWmzbuHFjQOFG165dqyxdulRZv369d9uSJUsCOldRehYS1el0dul4+xd/V5qeGqY4D3/hd3/hvr3Ks1dfphTs29PtNnWXrmqJVNSiQ1GElp4SknDtshz0MnuLQLX4+xx7HA7al0svvZRffvkFcI93/fTTT8ydO7fT84qKijh+/Dgvv/wy77//Pjt37uyad+ohpaWlAR8rl+fh+u4lNKfNRztkqt9jenMKaFe0RDJq0QFCSyRyKsUCCgYBOYC7776bl19+GXB3PUtKSlizZk2n5/nLBxBOPF3wzlAUGcenfwRjH/QzH2j3uEpLIaa4PpgTw79sPlAtkY5adIDQEom090A1GgmHloAcwKhRo/jXv/7lfT99+nTvdKaOmD59OiUlJd58AAsXLqSoqIiSkpJO8wmHE9feN1CO7UE/4wEkc/vTOysshSRnhjcGkEAgEISKgB4Ctw4U9dZbb2E2mzs9z18+gISEBLZsaX/BRzDJzMzs9BilrhTn9r+hGTwFzZgF7R/XPANo5Ln+h4dCTSBaogG16AChJRIJ1wzDcBAOLQE5gIkTJ3LDDTeQnJxMfn4+FouFv//976FuW48JZAzNseURkF3o5jza4Z19Q1UltoaGXpkCCuoZ21SLDhBaIhElDFMnw0U4tATkAE4//XSef/5570rgcePGRUX0wOPHj3fYTlfeZ8gHN6Obfg+apI6Du1UUNT8ADnMQOA+daYkW1KIDhJZIxOFwqCYkdDi0BFx6XFxci5XAe/fu5cwzzwxFm8KCYqvDsflhpNRstBNv6PT4yuYZQMkDRRA4gUCgDtp1ABdffDH//Oc/GTZsGFdccQXV1dXefYqiUFVVxZ49e8LRxm6TktL+A13n9r9B4wn0i15E0na+MK3CUkhMfALm+IRgNjFgOtISTahFBwgt4UZRZGioAJcdtAaITUFqFfunu3fMjz32GH/84x9bDAPbbDaef/55EhISSEhIoKSkhIyMDK688kpeeukl9u/fz7PPPuu3vJqaGt58800+++wz3nzzTYxGI9XV1Tz55JNcfPHFXHDBBZ22KRw9mXZreO6557wBoi6++GJmzJjhjWmhKAoffPBByBvXU9oL0Spbvse19w20Z12PJn1sQGX1VggIDx2Fm40m1KIDhJZwoigySnkejndvRqktRorPQL/oJeg3soUT6E645aKiIjZt2sQFF1zgjVYAcM8997BgwQJmzJgBuGPzeGKYXXvttaxdu7bdMhMSEli+fDlvvPEGDz30EE899RSJiYmcffbZARn/7mrpKu06gL179zJy5EgAbrzxxjahSf2FR400jhw5QnZ2dottitOG49MHIH4Auqm/C6gcRVGosBSSPXVGKJoZEP60RCNq0QFCSzBx/fwezp/Wtbtfd97tOD++H6XWHdZDqS3G8e7N6C55AueOFd7jZFn22irdGVehPX1hp3V//vnn/OlPf+Ktt97yOoC8vDx++umnFnf4JpOJ+fPnA+5MXzNnuoPXrVy5kvj4eH766SceffTRFmXfeuutvPfee7z66qtcd93JyMKKovDee+9ht9vZsWMHDz30ENdddx333nsvNTU1vPjii7z88st8//332Gw2srKyKCgoYPv27dx1110MGzasU12B0O46gIceeojs7Gyys7MZM2aM93V2djajR4/moYceCkoDwo3ru/9DqTiE/sJHkQyxAZ1TX3ECR1NTrz0AFghOdSS92Wv8PSi1xUj6zqejd4Tdbker1TJ79mwOHDjgjbWUn5/f4ibXbrezbt06Xn75ZaqqqkhLSyMrKwun08m2bdu48MIL/cZGMxgMPP/886xatYpdu3Z5t2/dupXS0lIGDBhARkYGNTU1LF26lIKCAubNm0ddXZ03ztAVV1zBt99+i9Fo5OGHHw5qKP52ewB/+tOf+PDDD5k0aRJz5sxp0R1xuVxs3LgxaI0IFa3TVsoVh3B++wKa0ZeiHXZBwOV4ZgD15hBQNKTgDAS16AChJZhoT1/Y4d26Ul+OFJ/RwglI8RlI8RkYrzkZmdhms3Upjv6WLVtwOBxs2LCB0aNHs379em699VYyMjIoKiry9igMBgOpqans3r27xboonU7H/PnzWbhwIbfffjujR49uU0dqairPPPMMd911F1dddRXgdjADBgxg2rRpTJs2DVmWSU5O5s4772To0KFccsklbN26Fa1WiyRJXH311dx7772sW7eOf/zjHwHr64x2ewDXXHMNb7zxBhMmTOCtt97iu+++Y+DAgYwePZoxY8Zwww2dz5xpamrimWeeYfPmzTz99NPU19ezbt063nnnHV5++eU2IVKDTVZWlve1O9zD/4AuBv2sB7tUjmcGUEovzgDy1RLNqEUHCC1hJTYF/aKXkOIzAE4+A4ht+fC6q0lUSkpKuOGGG1i0aBEPPvgg77zzDi6Xi3HjxjFs2DDef/9977GSJLVZK9TY2Mj48eNZu3Zti2gJrRk7dix33HGHN4LC4MGDeeONN7BarRw6dIiioiKSkpKIjY0lLy+P66+/nhdffJHTTjsNcMdqeuWVV5gwYQIbNmzoksaO6DQUxPTp0/nzn//Meeedx3PPPcc//vEPSkpKAnoG0DofwPr169m1axdXXHEFffv25ZNPPgmKiPbIzc31vnbtW4ti2YVuxn1IsX27VE6FpRBzYhKmuD7BbmLA+GqJZtSiA4SWcCJJGqR+IzEsWY9x+XYMS9YjtXoADHhDJAfC22+/TUFBgTdktCzLNDY28uyzz2K321mxYgX79u3jhRde4N133+X777/3PhD24HQ6uffee9mxYwdXXnlli33btm1j586d3hmUixYt4pprrgHcaXYHDRrERRddxObNmxk82D28PG/ePMaPH8+AAQOYOnWq9znsxo0befHFF9FoNN7EM8FAUrqw3Ky+vp5//etfrF69mkWLFrV54NGa48eP8+tf/5oVK1bw6aefYjKZqKur47bbbuPnn3/mtdde46mnnuqwjJ9++sk7Hao7+QCys7OpKz2M+d1rsCcMQVmwEr3B0KX45Xtf/T80egNjr7keCH2cefCfD2Ds2LFRF2e+9XWyWq2YzWbV5ANISEhQTT6Avn37hjUfQFlZGSNGjAgoH4DJZGoRy99oNPrNB2C32935AwLIB2AymXA4HG3K8HwXQlmGb9z+mJgYbDZbm3wADocDSZI6zQdw8OBBxo4d2yYfQEAP9QOJGV1cXKw88cQTyllnnaXMmTNHef3115XGxsaA4k375gP417/+pbz22muKoihKXl6e8qtf/arT83sSM9xzru3925Wmp7MVV8XhLpchu1zKP6+7Utm+6v+63Y5gEIrY6b2BWnQoitASiXUGapeigUC19CQfQIcrDfbt28crr7zC5s2bGT9+PE8++SSzZs1CkqSAUsj55gO48cYbmTx5stdDNTQ0kJiY2LmH6gGjR4/Glf8f5NyP0Z3/OzTJQ7pcRu2JMpw2W68+AAb8PlyKRtSiA4SWSCTUOXTDSTi0dPgQePHixeh0Ot5++21ef/11Zs+e7X0Ism3btk4Lb50PAPB2v/Pz85k6NbSRNYuP5OHY/CekviPQTv5Nt8qoLCoCeicJjC+eLna0oxYdILQEAyXIAc/UEtQOAtPS08+v3R7A8ePHufrqq0lKSmLbtm0tDL7D4eDzzz9n9uzZHRY+ffp0nn32WW8+gKVLl/Lhhx+ybt06Tpw4wU033dSjxreHZ8l4msmONOshSByIpO1eaFVPFrDkzIHBbGKX8YwFRztq0QFCS08xmUxUVFSQkpIStBwbp1JCGEVRqKio6FFPoV0H8NRTTzFp0qR2T2z9NNwf/vIBLFmypKtt7BLtLRlXFLnNjIFAqCgqIC65L8bY4C2+EAgE7ofjFouF8vLyoJXpcDjQ6zuP7RUNBKLFZDL1KJdDuw6gI+MPRG4k0IYKr/GHk0vGDUvWQ1zXw1dUWgpJHti7d//gngWiBtSiA4SWnqLX6xkypOvP5TqioaGB2NjAVvhHOuHQ0vVb4kjHZfe7ZBzZ3uWiZNlFZbGl18f/oWvzmyMZtegAoSUSUYsOCI+WqHAA1dXVfPnll975yx2iNXhXC3qQ4jNA0/VnALVlx3E57L0+Awjwzv+OdtSiA4SWSEQtOiA4Wjw52T1rQVoTcQ7g2LFjbNy4kccee4wrrriCn376iaSkJKZNm8bjjz/eeQEBLhkPBM8D4EjoAQgEAkF7KIpCfn4+69at44EHHuDgwYOkpaUxYMAALrjgAt577z2/53VpJXCo2bBhAwsWLPC+Hz58OK+88goOhwOz2UyfPn3ahKX2jwIuFyADGtBqgeDMMhAIBIJI49ixYxw7dgxwr5zOzMxk1apVjB8/ngkTJnD++ef7nWkVUQ6grKyMDz74gFGjRjFu3LiIT1IhEAgEkcCBAwfYsWMH48aNY8yYMQFPDY0oByAQCASC8BFxzwAEAoFAEB6EAxAIBIJTFOEABAKB4BRFOACBQCA4RREOQCAQCE5RhAMQCASCUxThAAQCgeAURTgAgUAgOEUJqQPYvXs3kydPZubMmZx33nmsXbsWgEOHDgWcDKaiooItW7bw97//nT179oSyuQKBQKAKFEWhqKiIDz74gKeffrrdjG8d5gTuKXq9nm+//RZJknjppZeYNWsWdrudr7/+ukX2eg8ul4uPPvqIH374gT179rBnz54WDX/88ccZP358KJssEAgEUUdtbS0fffQRe/bsYe/evezZs4eKigoANBoNY8aM8Zs4JqQOYNy4cd7XlZWVpKSk8Oabb3LVVVexefPmNsevWbOGZcuWodFoGDVqFNOmTeO2227D5XJhMpkwmUz88ssv2O3u2P4ajQaDwdAibrbJZMJms6EoCoqioNfpqCwqwBifgDGuDzqdDkmSvPk2NRoNer0em83mtwxwZzZzOp3eFG2eLD2eMrRaLTqdzluGJEkYjcaglOHRpigKJpMJh8OBLMveMhRFwel0esvQarXez6d1GR5tdrvdW4bBYECWZW8ZOp0OjUbT6Wfc3TIURUGSpDafscFgwOVyeT+faLhOiqKg0WjafMZGozHqrpOiKGi12g5/T9Fwnex2O5Ikdfp7iobr5HA4kCQpoDLKyspwuVyMGzeOyZMnk5aWxr59+zjzzDMZO3YscXH+MxqG1AF4sFgsZGZmsmPHDiZOnEhMTIzf4xYvXsyZZ57J8OHDMZvNgDuxfHZ2drfrLvplH1tf+38seOARBo0VvQeBQKA+GhoaOHLkCKNGjUKv15OTk8Ott97a6XlheQi8ZcsW5syZw9q1a3n00UdZunQpOTk5/Otf/2pxnE6nY+zYsV7j31MOHz5MZZEnqXt0x/Q/fPhwbzchKKhFBwgtkYhadEDXtMTGxnL66ad3OR9yWHoAx48fp3///jzzzDPebUuXLuWWW24Jab02m41KSyHG2Fhik5JDWleo8e1SRzNq0QFCSySiFh0QHi0h7wGUl5eTmpoa6mrapcJSSHLmIL/JEAQCgeBUJuLzAfTkGYDNZmPVrTcw4tzzmfnrzsfDIhm73Y7B0PW8xpGGWnSA0BKJqEUH9ExLoHZT1QvBjh09gq2hXhU5fSsrK3u7CUFBLTpAaIlE1KIDwqNF1Q6gOD8PgOTMgb3ckp5TVVXV200ICmrRAUJLJKIWHRAeLap2AA1lxwFU0QMQCASCYKNqB+Cqr8XUpw8xCYm93ZQek56e3ttNCApq0QFCSySiFh0QHi2qdgA1JcdIyRysihlAGo06LpVadIDQEomoRQeER4t6Pq1WKIpCVXEhyQPVMfxTXFzc200ICmrRAUJLJKIWHRAeLap1APWVFThtNjH+LxAIBO2gWgdQUVQARH8ICA8JCQm93YSgoBYdILREImrRAeHREtJQELt37+a2224jNjYWq9XK9ddfz+HDh/n555+59dZbmTt3btDrbKqrRWcwUnXMHUZaLT2Afv369XYTgoJadIDQEomoRQeER0tIewCefACff/45y5YtY/LkyTz++OOsXLmSTZs2haROp83Gt+vWMPD0cYyft5CY+PiQ1BNu8vPze7sJQUEtOkBoiUTUogPCoyWs+QA870tLS1m6dGlAZTidTnJycgBITU3FZDJRWOiO8BkXF0dmZiYHDhwA3E/N05MS2fPR++z7dCNjZsyh7kQ5TknD8fJyFEWhf//+6PV6b6KZ+Ph40tLSyMtzLxrT6XSMGDGCw4cPe4MxDRs2jMrKSu/CjPT0dDQajfchTUJCAv369fNeML1ez/Dhw8nPz/fGOB8+fDjl5eXU1NQAkJGRgSzLlJSUAJCUlERycjKHDh0C3LHKhw4dysGDB3E6nVitVlwuF6WlpdTW1gKQmZmJw+Hg+PHm9Q4pKcTHx3PkyBEAYmJiyMrKIjc31xtrfPTo0VgsFurr6wEYNGgQVquVsrIyAPr27UtcXBxHjx4FwGw2M3jwYO81AMjOzqagoIDGxkYAsrKyqK+v58SJE51eJ6vVSm5uLqNGjeLo0aPexEBDhgyhtrbWm8QiGq6T1Wrl8OHDLa4TwMiRI6PuOlmtVoqKitr8nqLtOlmtVnJycjr9PUXDdfJoCcTutb5OgRKWWEAWi4WtW7eydOlSioqKeOCBB0hOTubZZ5/t9NyuxgKqLTvOqjt/431viDFzxZ8eJ6F/OoZ28hBEA/n5+QwfPry3m9Fj1KIDhJZIRC06oGdaIioWkCcfAMDAgQNZvXo1hw4dCmmsC61ez/h5C1ny9AoS0zOi2vgDqvlSq0UHCC2RiFp0QHi0hMUBePIBeCvVaBg3blxInnJLGg3j5y1k2TMvMWLGHOJS+qI3GoNeT7hRy9imWnSA0BKJqEUHqOAZALTMB7Bq1SoOHTrEhAkTuPrqq9FqtUGvT2c0cs5Vi9EbjRTl5JCWkRH0OnoDz9hntKMWHSC0RCJq0QHh0RJyB9CvXz9uuOEGAK6//vpQV0dMH3XM+hEIBIJQo+qEMA6Ho8s5MiMVtWhRiw4QWiIRteiAnmmJqIfAvUV5eXlvNyFoqEWLWnSA0BKJqEUHhEeLqh2AZ46wGlCLFrXoAKElElGLDgiPFlU7AIFAIBC0j6odQIZKZgCBerSoRQcILZGIWnRAeLSo2gF4lmurAbVoUYsOEFoiEbXogPBoUbUD8MRvUQNq0aIWHSC0RCJq0QHh0aJqByAQCASC9glrPoBbbrmFXbt28csvvzB16lQefvjhUFZPUlJSSMsPJ2rRohYdILREImrRAeHRElIH4MkHIEkSL730EikpKTz55JNIksSCBQvYt28fY8eODVn9ycnJISs73KhFi1p0gNASiahFB4RHS1jzAdx8883e9yNHjgwo401X8wH4xsW2Wq1kZ2dHdPzyruQDGDt2bETHLw80H4DZbI66OPPt5QNISEiIujjz7eUD6Nu3b9TnAzhy5Agmk0kV+QAsFgsmkymk+QBQwkBRUZHy6quvet/X1dUpf/nLXwI6d//+/d2utyfnRhpq0aIWHYoitEQiatGhKOGxfWHPBwCwYcMG7rzzzpDXa1RBGGgPatGiFh0gtEQiatEB4dES9nwAW7ZsYfbs2cTFxXm7oqFi6NChIS0/nKhFi1p0gNASiahFB4RHS8gdgG8+gDVr1vD4449zyy23cNlll7Ft27aQ1n3w4MGQlh9O1KJFLTpAaIlE1KIDwqMlrPkArr32Wq699tpQV+nF87BHDahFi1p0gNASiahFB4RHi1gIJhAIBKcoqk4I43K5QpJ2sjdQixa16AChJRJRiw7omRaREAYoLS3t7SYEDbVoUYsOEFoiEbXogPBoUbUD8CzwUANq0aIWHSC0RCJq0QHh0aJqByAQCASC9lG1A8jMzOztJgQNtWhRiw4QWiIRteiA8GhRtQPwxA1RA2rRohYdILREImrRAeHRomoH4AnqpAbUokUtOkBoiUTUogPCoyWkDmD37t1MnjyZmTNnct5557F27Vp27drFsmXLQlmtQCAQCAIgrPkAZs2aRUpKijcsbKhJSUkJSz3hQC1a1KIDhJZIRC06IDxawpoPwCNIr9cHXEZP8gHIskx8fHxExy8PNB+ALMukpKREdPzyQPIByLJMVVVV1MWZ93edZFmmvr4+6uLM+7tOsixjs9miPh9AeXk5FRUVqsgH4NGiunwAS5YsCfhckQ/AjVq0qEWHoggtkYhadCiKivMBCAQCgaD3CXs+gHASExMT9jpDhVq0qEUHCC2RiFp0QHi0hDUfAEBubi6FhYXeMcJQkpWVFfI6woVatKhFBwgtkYhadEB4tITcAfjmAwAYNWoU27dvZ+TIkaGumtzc3JDXES7UokUtOkBoiUTUogPCo0XVC8E8T+rVgFq0qEUHCC2RiFp0QHi0qNoBCAQCgaB9VJ0QRlEUJEkKcot6B7VoUYsOEFoiEbXogJ5pEQlhwLs4RQ2oRYtadIDQEomoRQeER4uqHYBndZ4aUIsWtegAoSUSUYsOCI8WVTsAgUAgELSPqh3AoEGDersJQUMtWtSiA4SWSEQtOiA8WlTtAKxWa283IWioRYtadIDQEomoRQeER0tIo4Hu3r2b2267jdjYWKxWK7/97W+pr68nOTmZ+vp6lixZEsrqKSsrU014WLVoUYsOEFoiEbXogPBoCWkPwJMP4PPPP2fZsmUMGTKE6upqFixYQE1NDT/++GMoqxcIBAJBB4RtHcATTzzhjcm9YMECPv30U3Jzc7nzzjs7PG/v3r0YjcZwNFEgEAhUgc1m48wzz+z0uJAOAXmwWCxkZmaSl5dHQkIC4E7Q4ElM0RGBiBAIBAJB1wlrPoDk5GRvxpqGhgYSExPDUb1AIBAI/BDWfADTpk3zRrjLz89n6tSp4aheIBAIBH4Iaz6As846C6PRyPr164mPj2fSpEmhrl4gEAgE7RDxweAEAoFAEBpUvRBMIBAIBO0jHIBAIBCcoggHIBAIBKcoqnUAr7zyCu+//z6vv/56bzelW3z//fdMmTKF888/n0OHDkWdnl27drFs2TLAndpuxYoVbNiwgffee6/dbZGKrxaAjRs3MmXKFGbMmEFtbW1UaKmvr+fOO+9k1qxZPPzwwwCsW7eOd955h5dfftmbftDftkjDnxaLxcL555/PlClT2LFjBxAdWqqrq7nvvvuYN28emzZtAvzbrlD9/lXpAHbv3h31ISd27tzJV199xVdffUVVVVXU6Zk0aRI2mw2ADz/8kH79+jF//nz27NlDSUmJ322Riq8WRVEoLCzk66+/ZuvWrcTHx0eFlh9//JEnn3ySjz76iG+++YZ9+/axa9currjiCvr27csnn3yCxWJpsy0S8adl06ZNbN26la+//przzjsvarRUVlby+OOPs3LlSjZt2uTXdoXSnqnSAXzxxRcMHToUgOHDh7N9+/ZeblHXqKioYMuWLcyaNYuvv/46avXo9XrAfT2GDRsGQFZWFjt27PC7LZLxaMnLy+Pjjz/m0ksvZf/+/YB/fZHGlClTMJvNxMTEMHLkSL744gsGDx4MnPxOff311222RSKttaSkpPDVV18xY8YMPvjgA4Co0TJ06FA0Gg2lpaUsXbrU7289lL//sISCCDdVVVVdDjkRSaSkpPDOO+9w8OBB7rjjDiZNmhTVeqqqqoiPjwdOtt/ftmhg1KhRfPDBB+zevZs//OEPfPTRR1Glpb6+nvT0dHQ6HXFxcUDLaxJN3zOPloyMDF599VVKS0u56aabOOOMM6JKS1FREU8//TTJycnEx8e3abeiKCHTosoegFpCTowYMYIrrriCkpKSqNaTlJTkjW3uab+/bdHExIkTmTRpEjU1NVGlZcOGDdx5551+fyPR9rvxaPGQlpbG8uXLOXjwYFRpGThwIKtXr+bQoUPodLqwXhdVOoBoDznhuzZPr9dzyy23RLWe6dOne9t/5MgRzjvvPL/bogHfa5Oenk5iYmLUaNmyZQuzZ88mLi6OKVOmkJ+fD5z8Tp1//vlttkUqvlo8d8ngjoI5bty4qNICoNFoGDduHPPmzWvzWw+lPVPtSuAXXniB1NRU6urquOGGG3q7OV3i448/Zs2aNcycOZNzzjmH0047Ler05ObmctNNN/H//t//Y9iwYTz77LMMGjQIRVG46qqrcLlcbbZFKr5avvrqK7777jvOPfdcZs6cyaBBg6JCy5o1a1i5ciWJiYk4HA6WLVuG1Wr1DincdNNNaLVaXn/99TbbIo3WWubOncuWLVu46KKLGD9+vDfETDRoWbVqFYcOHWLChAkMGzaMsWPH+v2th+r3r1oHIBAIBIKOUeUQkEAgEAg6RzgAgUAgOEURDkAgEAhOUYQDEAgEglMU4QAEAoHgFEU4AIFAIDhFUWUoCIEgHCxfvpwzzjiD2tpafv/733vjBQkE0YLoAQgE3aC6uppzzz0XvV6PzWYTxl8QlYiFYAKBQHCKInoAAkEALF26FIvF4n3/8ccfc9VVVzF//nwuvfRSVqxY4fc4gSCSEc8ABIIu8t577/Haa6/xwgsvkJaWRlNTE+vXr+/tZgkEXUY4AIGgC9TX1/Pkk0+yfv160tLSAIiJiWHp0qW93DKBoOuIISCBoAts2bKFsWPHMnDgwN5uikDQY4QDEAi6QF5eHtnZ2b3dDIEgKAgHIBB0AbPZjCzLvd0MgSAoCAcgEHSBadOm8cknn3jzstrtdtauXdvLrRIIuod4CCwQdIGxY8dy++23c+ONNyLLMk6nk8svv7y3myUQdAvhAASCLrJgwQIWLFjQ280QCHqMGAISCASCUxThAASCAFi4cCHx8fFBO04giARELCCBQCA4RRE9AIFAIDhFEQ5AIBAITlGEAxAIBIJTFOEABAKB4BRFOACBQCA4RREOQCAQCE5RhAMQCASCU5T/D7LcdJRc84pvAAAAAElFTkSuQmCC"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "\n"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "72af5d6cef4929eebd83a185b8e102315bc9e9767996c122e83326ce225a6243"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.10 64-bit ('ptuning': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}